Paper_ID stringlengths 10 10 | Question stringlengths 201 1.81k | ocr_output stringlengths 252 54k ⌀ |
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OZitfSXpdT | In addition, the teacher model indeed may underperform on the outliers. According to previous discussion, directly decreasing the $\alpha$ also can solve this issue. Thus, why to introduce the inter-sample relations? This motivation needs to be further claified. | Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation
Chengming Hu\textsuperscript{1,2}\footnote{Equal contribution with random order.}, Haolun Wu\textsuperscript{1,2}\footnote{To whom the correspondence should be addressed.}, Xuan Li\textsuperscript{1,2}, Chen Ma\textsuperscript{3}, Xi ... |
TFR0GrzERG | In the introduction, the authors claim that task descriptions with minimal information can impair in-context learning performance because they hinder the model’s ability to learn from in-context examples. I don’t think that this is correct given the presented results (Figure 1). | On Task Description of In-context Learning: A Study from Information Perspective
Anonymous authors
Paper under double-blind review
Abstract
Transformers have demonstrated remarkable performance in a wide range of applications, making in-context learning an essential technique. Although the in-context learning has be... |
esqRHCwTJ2 | In other words, as the marginal feature distribution $P_X$ changes under strategic feedback, it would be expected that the qualification rate $Q(\mathcal{S}_{o,t-1})$ would also change in response, no? | Long-Term Impacts of Model Retraining with Strategic Feedback
Anonymous authors
Paper under double-blind review
Abstract
When machine learning (ML) models need to be frequently retrained, it is often too expensive to obtain human-annotated samples, so recent ML models have started to label samples by themselves. Thi... |
NddKiWtdUm | Additionally, given the use of simulation with LMs is touted as a benefit of this approach, what are the drawbacks of using this instead of collecting representative feedback (and training a reward model), whose values are the models implicitly aligning to with such simulation? These should be explicitly called out and... | Training Socially Aligned Language Models in Simulated Human Society
Ruibo Liu
Dartmouth College
Ruixin Yang
University of British Columbia
Chenyan Jia
Stanford University, Northeastern University
Ge Zhang
University of Michigan, Ann Arbor
Diyi Yang
Stanford University
Soroush Vosoughi
Dartmouth Colle... |
qTlcbLSm4p | Besides, from the equation and algorithm, I can’t find an explicit connection between low-resolution diffusion and high-resolution diffusion; such an explicit statement may be necessary for reader to understand. | Relay Diffusion: Unifying Diffusion Process Across Resolutions for Image Synthesis
Jiayan Teng∗1, Wendi Zheng∗1, Ming Ding∗12†, Wenyi Hong1, Jianqiao Wangni2, Zhuoyi Yang1, Jie Tang1†
∗equal contribution 1Tsinghua University 2Zhipu AI †corresponding authors
{tengjy20@mails,zhengwd23@mails,jietang@mail}.tsinghua.edu.cn... |
RAA0vCLMhp | In Table 4, the results of existing state-of-the-art solutions reported are zero-shot or fine-tuned, and SeMDiff is the zero-shot or fine-tuned? If the results of SOTA are zero-shot, how about the fine-tuned performance on these datasets? | Semantic Memory Guided Diffusion Networks for Image-to-Long Text Generation
Anonymous authors
Paper under double-blind review
Abstract
Automatic describing image with comprehensive textual content is often demanded by different real-world applications, which motivates image-to-text generation tasks such as image cap... |
LTHWoQ9ac1 | Sequential recourse: the problem in Eq (7) doesn’t seem like the real problem that needs to be solved. The set U_P updates after every step, and this changes the argmax A and therefore w_ij. Am I missing anything? | Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation
Anonymous authors
Paper under double-blind review
Abstract
Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. Most existing methods in the literature generate... |
oJ1tx3fXDA | In Eq.(5), what is the MVR term? Could the author explain this in detail? Its first part is a sum of $J_i$-step updates, while its second part is a sum of $j$-step updates. I know these two parts come from the additional term from $\theta_{i,j}^t-\theta_i^{t-\tau_i}$ to $\theta^{t-1}-\theta^{t-\tau_i-1}$, but how does ... | COMMUNICATION-EFFICIENT HETEROGENEOUS FEDERATED LEARNING WITH GENERALIZED HEAVY-BALL MOMENTUM
Anonymous authors
Paper under double-blind review
ABSTRACT
Federated Learning (FL) is the state-of-the-art approach for learning from decentralized data in privacy-constrained scenarios. As the current literature reports, t... |
gBLEHzKOfF | Proposition 3.1 assumes $Y \sim \pi_{\epsilon}^{\star} (\cdot | x)$ in Eq 7. In practice, Algorithm 1 adopts the mini-batch estimate for $\hat{\pi}_{ \epsilon}$. Can GENOT still recovers Optimal Conditional Generators by taking expectations over mini-batch estimate, e.g. [4]? In this respect, I am curious about the qua... | Generative Entropic Neural Optimal Transport To Map Within and Across Spaces
Anonymous authors
Paper under double-blind review
Abstract
Learning measure-to-measure mappings is a crucial task in machine learning, featured prominently in generative modeling. Recent years have witnessed a surge of techniques that draw ... |
WqsYs05Ri7 | Is there a way to abstract away the specific form of the concept activation of Oikarinen et al. (2023) in this formulation? Stated differently, what is $\vec{m}(x)$, and $\vec{s}(x)$ for a standard CBM? | ESTIMATION OF CONCEPT EXPLANATIONS SHOULD BE UNCERTAINTY AWARE
Anonymous authors
Paper under double-blind review
ABSTRACT
Model explanations are very valuable for interpreting and debugging prediction models. We study a specific kind of global explanations called Concept Explanations, where the goal is to interpret ... |
a745RnSFLT | Besides, using the pre-trained LLaMA can improve the PAC-Bayes bound, is it some form of transferring the ''generalization problem'' of the prompt engineering to the generalization problem of the pre-trained language model? | UNDERSTANDING PROMPT ENGINEERING MAY NOT REQUIRE RETHINKING GENERALIZATION
Victor Akinwande¹, Yiding Jiang¹, Dylan Sam¹ & J. Zico Kolter¹,²
¹Carnegie Mellon University, ²Bosch Center for AI
ABSTRACT
Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without a... |
SkETBJRKH7 | I am a bit confused about the results shown in the middle panel of Figures 4 and 5: The PFC-LLM architecture produced zero invalid action proposals in both tasks. Does this imply that the Monitor module is unnecessary, given that its role is to identify invalid action proposals? However, this contradicts the ablation s... | A Prefrontal Cortex-inspired Architecture for Planning in Large Language Models
Anonymous authors
Paper under double-blind review
Abstract
Large language models (LLMs) demonstrate impressive performance on a wide variety of tasks, but they often struggle with tasks that require multi-step reasoning or goal-directed ... |
y01KGvd9Bw | The major concern I have is the necessity to utilize the token from LLM for image decoding. What is is going to be if you let the LLM to first output the image description, then extract it and feed it directly to the diffusion model? | DREAMLLM: Synergistic Multimodal Comprehension and Creation
Runpei Dong \(^{1,2}\) Chunrui Han \(^3\) Yuang Peng \(^4\) Zekun Qi \(^{1,2}\) Zheng Ge \(^3\)
Jinrong Yang \(^5\) Liang Zhao \(^3\) Jianjian Sun \(^3\) Hongyu Zhou \(^3\) Haoran Wei \(^3\)
Xiangwen Kong \(^3\) Xiangyu Zhang \(^3\) Kaisheng Ma \(^4\) Li Yi \... |
kxpswbhr1r | The paper could benefit from a discussion on the practical implications of the findings. For instance, how can the insights on convergence be used to improve training methodologies or model selection in real-world applications? | In-Context Convergence of Transformers
Anonymous authors
Paper under double-blind review
Abstract
Transformers have recently revolutionized many domains in modern machine learning and one salient discovery is their remarkable in-context learning capability, where models can solve an unseen task by utilizing task-spe... |
dEz3ge8QSo | In section 2, the authors introduce convex risk measures, where there is only one input in the measure $\sigma$ in (5) and (6), while there are two in examples 1 and 2. Could the authors explain what is the difference between these two $\sigma$'s? | SOFT ROBUST MDPs AND RISK-SENSITIVE MDPs: EQUIVALENCE, POLICY GRADIENT, AND SAMPLE COMPLEXITY
Runyu (Cathy) Zhang
Harvard University
runyuzhang@fas.harvard.edu
Yang Hu
Harvard University
yanghu@g.harvard.edu
Na Li
Harvard University
nali@seas.harvard.edu
ABSTRACT
Robust Markov Decision Processes (MDPs)... |
mOTiVzTgF2 | The discussion of Adam condition number assumes very very small values of gradient, which is not observed realistically. This discussion should be contextualized given realistic values of the gradient. | ResiDual: Transformer with Dual Residual Connections
Anonymous authors
Paper under double-blind review
Abstract
Transformer networks have become the preferred architecture for many tasks due to their state-of-the-art performance. However, the optimal way to implement residual connections in Transformer, which are es... |
1vDArHJ68h | In Appendix G (BSuite environment), is there any hypothesis on why sometimes harder environments (longer memory steps) present better performance than easier ones? For instance, R2I’s performance on 31 memory steps is better than 15 memory steps. Similarly, the performance in 81 memory steps seems better (or more stabl... | MASTERING MEMORY TASKS WITH WORLD MODELS
Mohammad Reza Samsami∗1,2 Artem Zholus∗1,3 Janarthanan Rajendran1,2 Sarath Chandar1,3,4
1Mila – Quebec AI Institute 2Université de Montréal 3Polytechnique Montréal 4CIFAR AI Chair
ABSTRACT
Current model-based reinforcement learning (MBRL) agents struggle with long-term depen... |
cYksYKbf6K | Maybe I’m missing something, but I do not see why “Our method is computationally simpler compared to previous methods”. Despite being conceptually straightforward, the method requires running a complete rollout up to the maximum length before any truncation occurs. Could you elaborate on the computational saving? | Imagine Within Practice: Conservative Rollout Length Adaptation for Model-Based Reinforcement Learning
Anonymous authors
Paper under double-blind review
Abstract
Model-based reinforcement learning (MBRL) algorithms achieve high sample efficiency by leveraging imagined rollouts from a world model for policy optimizat... |
5KUiMKRebi | - I wonder about the integer inputs (i.e. the tensor coordinates) to the INR network. Do you normalise these coordinates somehow or directly input the integers into the INR network? Did such an integer input space cause any problems during training? | Implicit Neural Representation Inference for Low-Dimensional Bayesian Deep Learning
Panagiotis Dimitrakopoulos¹, Giorgos Sfikas² & Christophoros Nikou¹
¹Dept. of Computer Science & Engineering, University of Ioannina, Ioannina Greece
²Dept. of Surveying & Geoinformatics, University of West Attica, Athens Greece
¹{p.di... |
OinvjdvPjp | Could you please demonstrate how well the proposed method can handle situations where it needs to refer to previously mentioned real numbers in the context, ensuring these numbers remain unaltered? How does this embedding method impact a Language Model's capability to preserve real numbers in the given input? | xVal: A Continuous Number Encoding for Large Language Models
Anonymous authors
Paper under double-blind review
Abstract
Large Language Models (LLMs) have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers. We propose xVal, a numerical enc... |
cZo6pDtDZr | As best I can tell, the lower bound in Theorem 3 is not really comparable to the upper bound in Theorem 2 since one imposes $\beta = 0$ in the former while the upper bound requires $\beta>0$. For instance, when $\beta>0$, it does not seem to be necessary to have any dependence on $\alpha$, since it seems to me that sim... | NEAR-OPTIMAL ALGORITHMS FOR PRIVATE ESTIMATION AND SEQUENTIAL TESTING OF COLLISION PROBABILITY
Anonymous authors
Paper under double-blind review
ABSTRACT
We present new algorithms for estimating and testing collision probability, a fundamental measure of the spread of a discrete distribution that is widely used in m... |
4Qz9BT4mpM | Could you elaborate on the theoretical underpinnings of the 'agreement-on-the-line' method? How does it theoretically and practically differ from existing methods for assessing model performance on out-of-distribution data? | Predicting the Performance of Foundation Models via Agreement-on-the-Line
Anonymous authors
Paper under double-blind review
Abstract
Estimating out-of-distribution performance is critical to safely deploy machine learning models. Recently, Baek et al. showed that the phenomenon “agreement-on-the-line” can be a relia... |
77N93tc3o5 | In the manuscript, it sounds like identifiability should follow trivially from previous works, such as the iVAE framework (Khemakhem et al., 2020). Even if this were the case, it would be helpful to restate and discuss the required assumptions. | DEEP INDEPENDENT VECTOR ANALYSIS
Anonymous authors
Paper under double-blind review
ABSTRACT
We introduce a deep multivariate latent variable model, Deep Independent Vector Analysis (DeepIVA), for learning linked and identifiable latent sources across multiple data modalities by unifying multidataset independent subs... |
IcR1OOFzxm | One thing I'm not particularly sure is how is the answer selected in RAISE. When you generate the answer, how do you pick the candidate from the given set? PrAE and ALANS actually only generate the hidden latents and compare in the latent space. Do you compare in the pixel space? Do you think comparing in the hidden sp... | Towards Generative Abstract Reasoning: Completing Raven’s Progressive Matrix via Rule Abstraction and Selection
Fan Shi Bin Li∗ Xiangyang Xue
Shanghai Key Laboratory of Intelligent Information Processing
School of Computer Science, Fudan University
fshi22@m.fudan.edu.cn {libin,xyxue}@fudan.edu.cn
Abstract
Endo... |
8sKcAWOf2D | The section detailing the use of CMAP to patch activations from Goat-7B to Llama-7B lacks clarity, particularly when validating the impact of QKV on the model's performance. The rationale behind why patching the QK-circuit of the Value Fetcher and the value vector of the Position Transmitter heads results in the most s... | Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking
Nikhil Prakash¹* Tamar Rott Shaham² Tal Haklay³ Yonatan Belinkov³ David Bau¹
¹Northeastern University ²MIT CSAIL ³Technion – IIT
Abstract
Fine-tuning on generalized tasks such as instruction following, code generation, and mathematics has been... |
qODvxQ8TXW | While the authors argue LRR finds a better mask than WR in Figure 3, I wonder if a longer training epochs within each IMP cycle would help WR to find a superior mask. In other words, are both WR and LRR fully converged? | Masks, Signs, And Learning Rate Rewinding
Advait Gadhikar & Rebekka Burkholz
CISPA Helmholtz Center for Information Security
Saarbrücken, Germany
{advait.gadhikar, burkholz}@cispa.de
Abstract
Learning Rate Rewinding (LRR) has been established as a strong variant of Iterative Magnitude Pruning (IMP) to find lottery t... |
VAvSUG3hwI | If the agent has to be trained step by step, could this affect how useful the solution is in situations where the agent must work with many teammates simultaneously? For instance, think of an online chatbot on an e-commerce platform where customers show up all together. | One by One, Continual Coordinating with Humans via Hyper-Teammate Identification
Anonymous authors
Paper under double-blind review
Abstract
One of the primary objectives in modern artificial intelligence researches is to empower agents to effectively coordinate with diverse teammates, particularly human teammates. P... |
G0vdDSt9XM | The paper could provide a more in-depth analysis of the tool creation and retrieval components of CRAFT. Understanding how different types of tools contribute to performance improvements and how the retrieval mechanism interacts with various tasks would offer valuable insights. | CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
Lifan Yuan∗, Yangyi Chen∗, Xingyao Wang, Yi R. Fung, Hao Peng, Heng Ji
University of Illinois Urbana-Champaign
{lievanyuan173}@gmail.com
{yangyic3,xingyao6,yifung2,haopeng,hengji}@illinois.edu
Abstract
Large language models (LLMs) are often... |
tAcEidZ1Y2 | Although reconstruction is an important topic in MRI, are there any other reasons that make the proposed method tie to MRI reconstruction? In other words, whether the proposed method is suitable for potential reconstruction tasks in natural image domains? | Self-supervision Meets Bootstrap Estimation: New Paradigm for Unsupervised Reconstruction with Uncertainty Quantification
Anonymous authors
Paper under double-blind review
Abstract
Deep learning-based self-supervised reconstruction (SSR) plays a vital role in diverse domains, including unsupervisedly reconstructing ... |
lOsF9k1sxW | In the relevant work, it has been written that the previous defense methods have high calculation costs, which limits their practicability in the actual environment. But won't the calculation of FIM in SFT increase the complexity and cost of calculation? | FISHER INFORMATION GUIDED BACKDOOR PURIFICATION VIA NAÏVE EXPLOITATION OF SMOOTHNESS
Anonymous authors
Paper under double-blind review
ABSTRACT
Backdoor attacks during deep neural network (DNN) training have gained popularity in recent times since they can easily compromise the safety of a model of high importance, ... |
Y8OaqdX5Xt | How does PToM perform compared with an agent that performs neurally-guided MCTS trained via self-play like AlphaZero, and how much of the fast convergence benefit comes from model-based planning vs. ToM? | PLANNING WITH THEORY OF MIND FOR FEW-SHOT ADAPTATION IN SEQUENTIAL SOCIAL DILEMMAS
Anonymous authors
Paper under double-blind review
ABSTRACT
Despite the recent successes of multi-agent reinforcement learning (MARL) algorithms, efficiently adapting to other agents in mixed-motive environments remains a significant c... |
WSzRdcOkEx | The definition of robustness in Eq. (1) and Eq.(4) seems to be confusing and possibly wrong. In Eq. (1), $\Delta_{\min}$ is defined as the minimal perturbation of a sample-label pair causing the change of the top-1 class prediction. Then, I understand $g(x)$ is a lower bound of $\Delta_{\min}$. However, in Eq. (3), $g(... | GREAT Score: Global Robustness Evaluation of Adversarial Perturbation Using Generative Models
Anonymous authors
Paper under double-blind review
Abstract
Current studies on adversarial robustness mainly focus on aggregating local robustness results from a set of data samples to evaluate and rank different models. How... |
LOyYjE0blM | The top and bottom molecule in Fig 1c are identical yet lead to different phenotypes. The title of the figure says that different molecules yield different phenotypes in cells. If there was an intention for some of these aspects, could you possibly clarify it? If not, perhaps drop the second copy of the molecule. | Neural scaling laws for phenotypic drug discovery
Anonymous authors
Paper under double-blind review
Abstract
Recent breakthroughs by deep neural networks (DNNs) in natural language processing (NLP) and computer vision have been driven by a scale-up of models and data rather than the discovery of novel computing para... |
lygdvIKDxi | How does the similarity of the public dataset to the target model’s dataset affect results? The main results in Table 1 show significant overlap: CIFAR-10 is used as the public dataset when CIFAR-100 is the hidden dataset, and vice versa, and even Tiny-ImageNet has strong similarities compared to CIFAR 10/100. | SEEKER: SEMI-SUPERVISED KNOWLEDGE TRANSFER FOR QUERY-EFFICIENT MODEL EXTRACTION
Anonymous authors
Paper under double-blind review
ABSTRACT
Model extraction attacks against neural networks aim at extracting models without white-box access to model internals and training datasets. Unfortunately, most existing methods ... |
RIEW6M9YoV | In Table 2, we can observe that the novelty of the generated molecules is low compared to those of the baselines (mainly on QM9). I would expect the authors to provide some explanation or intuitions about why the proposed model fails to produce novel graphs. | GRAPH GENERATION WITH $K^2$–TREES
Yunhui Jang, Dongwoo Kim, Sungsoo Ahn
Pohang University of Science and Technology
{uni5510, dongwookim, sungsoo.ahn}@postech.ac.kr
ABSTRACT
Generating graphs from a target distribution is a significant challenge across many domains, including drug discovery and social network analys... |
EODzbQ2Gy4 | If the method is not intended to transfer to different behaviours (only different object displacements), why not just perform planning on the target task directly? TransferStep itself appears to be a form of trajectory optimisation at each step, and at least for the tasks presented in the experiments (which are all rel... | DIFF-TRANSFER: MODEL-BASED ROBOTIC MANIPULATION SKILL TRANSFER VIA DIFFERENTIABLE PHYSICS SIMULATION
Anonymous authors
Paper under double-blind review
ABSTRACT
The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce Dif... |
2lDQLiH1W4 | Let's say we input a set of views V = [v1, v2, v3, v4] and generate a shape A. Then we multiply all the views with a transformation matrix M and generate a shape B. Will shape A and shape B under the same canonicalized coordinate frame? | Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model
Jiahao Li1,2∗ Hao Tan1 Kai Zhang1 Zexiang Xu1 Fujun Luan1 Yinghao Xu1,3 Yicong Hong1,4 Kalyan Sunkavalli1 Greg Shakhnarovich2 Sai Bi1
1Adobe Research 2TTIC 3Stanford University 4Australian National University
{jiahao,greg}@ttic.edu y... |
dIjwC8A0N6 | The Lion optimizer demonstrates greater efficiency when employed with larger batch sizes and lower learning rates. It would be great if the authors can address how these specific characteristics of the Lion optimizer influence the training process within their experimental setup. | QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
Anonymous authors
Paper under double-blind review
Abstract
Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks. Fine-tuning these pre-trained models on downstream datasets pro... |
52fz5sUAy2 | why $r_{u,i}$ is affected by $o_{u,i}$? In my opinion, $o_{u,i}$ is just a treatment to observe $r_{u,i}$, and does not affect the 'value' of $r_{u,i}$. (i.e., the value of $r$ is affected only by $x$ and observed only when $o=1$). | Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
Haoxuan Li1 Chunyuan Zheng1 Sihao Ding2 Peng Wu3,* Zhi Geng3
Fuli Feng2 Xiangnan He2
1Peking University 2University of Science and Technology of China
3Beijing Technology and Business University
hxli@stu.pku.edu.cn dsihao@mail.ustc.edu.cn
... |
Spp2i1hKwV | ideally, rather than (arbitrarily?) choosing k = 18 & k = 100, you should provide an automated approach to AUTOMATICALLY assess the smallest amount of annotations that leads to the best possible performance; you only introduce Auto-IDEAL in | IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
Shaokun Zhang\(^1*\) Xiaobo Xia\(^2*\)\(^†\) Zhaoqing Wang\(^2\) Ling-Hao Chen\(^3\) Jiale Liu\(^4\)
Qingyun Wu\(^1†\) Tongliang Liu\(^2\)
\(^1\)Pennsylvania State University \(^2\)The University of Sydney
\(^3\)Tsingh... |
xRiZddh5Pb | The authors explain the idea behind designing the input features in Proposition 1, wherein a ranking metric using the designed distance input features following a specific format that meets the RankPres property. It is not straightforward to me why this format would satisfy the property. Detailed proof would be helpful... | Learning from A Single Graph is All You Need for Near-Shortest Path Routing
Anonymous authors
Paper under double-blind review
Abstract
We propose a simple algorithm that needs only a few data samples from a single graph for learning local routing policies that generalize across classes of geometric random graphs in ... |
xAqcJ9XoTf | In Figure 2, how did you compute the Lipschitz constant of MLPs? We can compute the Lipschitz constant for models that consist of a single layer, but exact computation of the Lipschitz constant of MLPs is NP-hard [1]. | ON THE STABILITY OF EXPRESSIVE POSITIONAL ENCODINGS FOR GRAPHS
Yinan Huang*1, William Lu*2, Joshua Robinson3, Yu Yang4, Muhan Zhang5, Stefanie Jegelka6, Pan Li1
1Georgia Institute of Technology, 2Purdue University, 3Stanford University, 4Tongji University, 5Peking University, 6MIT CSAIL
{yhuang903, panli}@gatech.edu, ... |
oGNdBvymod | This could be due to my limited understanding to Bayesian NN: For the image classification experiement, how do you compare the results obtained from sampling-based and optimization-based algorithm? Specifically, do you obtain certain point estimates of NN's weights from MCMC samples, and compute metrics on test dataset... | Entropy-MCMC: Sampling from Flat Basins with Ease
Bolian Li, Ruqi Zhang
Department of Computer Science, Purdue University, USA
{li4468,ruqiz}@purdue.edu
Abstract
Bayesian deep learning counts on the quality of posterior distribution estimation. However, the posterior of deep neural networks is highly multi-modal in ... |
qxLVaYbsSI | In the scenario of both unlabeled and labeled data, some combinations of federated algorithms and semi-supervised algorithms does not perform as well as federated learning using only labeled data, which violates normal cognition. The authors can try to explain the reasons for this phenomenon. | Robust Training of Federated Models with Extremely Label Deficiency
Yonggang Zhang\textsuperscript{1}\thanks{Equal contributions.} \quad Zhiqin Yang\textsuperscript{1}\thanks{Equal contributions.} \quad Xinmei Tian\textsuperscript{2} \quad Nannan Wang\textsuperscript{3} \quad Tongliang Liu\textsuperscript{4} \quad Bo ... |
TiY8Cvc2SR | In clinical practice the smaller the areas of tumor the more challenging the case is so degradation in performance in the datasets with smaller number of positive tiles may make it so this method is not the best choice for more challenging datasets. | PROGRESSIVE PSEUDO BAG AUGMENTATION WITH INSTANCE IMPORTANCE ESTIMATION FOR WHOLE SLIDE IMAGE CLASSIFICATION
Anonymous authors
Paper under double-blind review
ABSTRACT
In the field of computational pathology, the classification of whole-slide images (WSI) remains a challenging task due to the vast amount of gigapixe... |
s9bCeJGUJi | D3GAT is proposed as an adaptation of DDGAT to overcome the issue that DDCAT “jointly calculates the invariant pattern and the variant pattern of the node feature, which limits pattern learning, because they are decided jointly and cannot adjust with regard to the other.” This claim seems to be unjustified. Through rea... | CURRICULUM DYNAMIC GRAPH INVARIANT LEARNING UNDER DISTRIBUTION SHIFT
Anonymous authors
Paper under double-blind review
ABSTRACT
Dynamic graph neural networks have attracted intensive research interests recently but generally suffer from handling distribution shifts that widely exist in dynamic graphs. Although the e... |
3VD4PNEt5q | The methodology, while promising, seems to be narrowly tailored for a specific set of fusion models. This raises concerns about its universality. An in-depth exploration into its effectiveness against a broader spectrum of fusion models would have provided a more comprehensive perspective, allowing for a holistic under... | FUSION IS NOT ENOUGH: SINGLE MODAL ATTACKS ON FUSION MODELS FOR 3D OBJECT DETECTION
Zhiyuan Cheng¹ Hongjun Choi² Shiwei Feng¹ James Liang³ Guanhong Tao¹ Dongfang Liu³ Michael Zuzak³ Xiangyu Zhang¹
¹Purdue University {cheng443, feng292, taog, xyzhang}@purdue.edu
²DGIST hongjun@dgist.ac.kr
³Rochester Institute of Techno... |
9XdLlbxZCC | Missing analysis. From Table 4, we can see that the model's performance is quite sensitive to the used backbone (more than 10% between Rsenet50 and ConvNext. However, the authors didn't give explanation. Besides, the proposed model uses six loss terms in total for both flow estimation and content learning. I am wonderi... | MC-JEPA: A JOINT-EMBEDDING PREDICTIVE ARCHITECTURE FOR SELF-SUPERVISED LEARNING OF MOTION AND CONTENT FEATURES
Anonymous authors
Paper under double-blind review
ABSTRACT
Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, ... |
rpwES4pe9W | Including coordinate inputs inevitably increase the computational cost, as each coordinate has to go through a (usually larger) MLP network instead of grid sampling and small MLP feed-forward as multi-plane feature input. In fact, one key motivation of multi-plane methods is decreasing the training and rendering time r... | REFINED TENSORIAL RADIANCE FIELD: HARNESSED COORDINATE-BASED NETWORKS FOR NOVEL VIEW SYNTHESIS FROM SPARSE INPUTS
Anonymous authors
Paper under double-blind review
ABSTRACT
The multi-plane encoding approach has been highlighted for its ability to serve as static and dynamic neural radiance fields without sacrificing... |
1zhM0XkQh0 | The PGD-20 metric of the proposed method is pretty worse than the other SOTA in most cases but it is not adequately discussed or mentioned. Could the authors provide some intuitions why does such degradation on PGD-20 happen, any investigation to address that drawback? | PROFeAT: PROJECTED FEATURE ADVERSARIAL TRAINING FOR SELF-SUPERVISED LEARNING OF ROBUST REPRESENTATIONS
Anonymous authors
Paper under double-blind review
ABSTRACT
Supervised adversarial training has been the most successful approach for improving the robustness of Deep Neural Networks against adversarial attacks. Whi... |
IOrnCVIKIZ | In table 2 and table 5, are the pretrained model performances on HumanEval measured with or without postprocessing? I believe a more apples-to-apples comparison would be between Pretrained model w/ postprocessing vs LETI w/o postprocessing. Given that LETI seems to learn how to clean up syntax errors, it seems a more f... | LETI: Learning to Generate from Textual Interactions
Anonymous authors
Paper under double-blind review
Abstract
Finetuning pre-trained language models (LMs) is essential for enhancing their capabilities and is a crucial phase in their lifecycles. Existing techniques commonly fine-tune on input-output pairs (e.g., in... |
K804zYw6Wc | For GMM and LMM, NIR and RGB are concatenated along channel dimensions to input the same subsequent modules, and then split to obtain the two estimated NIR weights and RGB weights to apply to the corresponding branches, respectively. It seems to me that two estimated weights are the same, how to achieve selective fusio... | NIR-Assisted Image Denoising: A Selective Fusion Approach and a Real-World Benchmark Dataset
Anonymous authors
Paper under double-blind review
Abstract
Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light en... |
yqIJoALgdD | However, one of the appealing aspects of SNNs is their utilization of binary events for information processing. In essence, a spiking neuron is active only when it encounters spikes, enabling an event-driven regime and an energy-efficient system. However, the proposed neuron nodes deviate from this advantageous feature... | Towards Zero Memory Footprint Spiking Neural Network Training
Anonymous authors
Paper under double-blind review
Abstract
Spiking Neural Networks (SNNs), as representative brain-inspired neural networks, emulate the intrinsic characteristics and functional principles of the biological brain. With their unique structu... |
fjwZHuQ3cm | In Table 1, what is meant by Detectors $d$ having different attack names as subscripts to them such as $d_{\text{FGSM}}$? Is this detector tuned to defend against FGSM? If so, why is there only one detector for $LIMANS_{10}$ and not one for the claimed strongest attack of $\text{LIMANS}_{4000}$? What is SA (standard ac... | LIMANS: Linear Model of the Adversarial Noise Space
Anonymous authors
Paper under double-blind review
Abstract
Recent works have revealed the vulnerability of deep neural network (DNN) classifiers to adversarial attacks. Among such attacks, it is common to distinguish specific attacks adapted to each example from un... |
fvse7bMkAs | Although there are CLT results for the estimators, the validity of the statistical test is lacking --- in particular, the validity of the Bootstrap approximation. The CLT result is not sufficient for the validity of the test since the limiting distribution contains the unknown variance term; does Bootstrap approximatio... | RISK ASSESSMENT AND STATISTICAL SIGNIFICANCE IN THE AGE OF FOUNDATION MODELS
Anonymous authors
Paper under double-blind review
ABSTRACT
We propose a distributional framework for assessing socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical rel... |
3ROGsTX3IR | Am I right to understand the theory quantitatively predicts the experiments only in the GFL phase, and only holds qualitatively in terms of phenomenology to explain the transition to GMFL-I and II phases? | GROKKING AS A FIRST ORDER PHASE TRANSITION IN TWO LAYER NETWORKS
Noa Rubin∗ Inbar Seroussi† Zohar Ringel ‡
ABSTRACT
A key property of deep neural networks (DNNs) is their ability to learn new features during training. This intriguing aspect of deep learning stands out most clearly in recently reported Grokking pheno... |
KqbCvIFBY7 | The illustrated plots in Figure 1 are a bit misleading to me. Why are the initial points concentrated in one mode? In a high-dimensional setting, the chance of sampling close-by Gaussian points is low. | Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
Gabriele Corso∗1, Yilun Xu1, Valentin de Bortoli2, Regina Barzilay1, Tommi Jaakkola1
1CSAIL, Massachusetts Institute of Technology, 2ENS, PSL University
Abstract
In light of the widespread success of generative models, a significant amount of resea... |
XUCAA0XnPC | Additionally, the client is supposed to be computationally restricted, which is why a server is involved in the first place. How, then, is the client able to train the entire model locally (Section 4.2)? | ENSEMBLER: COMBATING MODEL INVERSION ATTACKS USING MODEL ENSEMBLE DURING COLLABORATIVE INFERENCE
Anonymous authors
Paper under double-blind review
ABSTRACT
Deep learning models have exhibited remarkable performance across various domains. Nevertheless, the burgeoning model sizes compel edge devices to offload a sign... |
ASppt1L3hx | The parameter (\kappa) for batch dependency is set as 256, however, Figure 4 indicates minimal difference between 64, 256, and even infinity. Furthermore, Figure 3 shows that the GNN model validation F1-score drops when \kappa is 256 (or larger). Given these observations, how do you justify the choice of \kappa as 256 ... | COOPERATIVE MINIBATCHING IN GRAPH NEURAL NETWORKS
Anonymous authors
Paper under double-blind review
ABSTRACT
Significant computational resources are required to train Graph Neural Networks (GNNs) at a large scale, and the process is highly data-intensive. One of the most effective ways to reduce resource requirement... |
PCm1oT8pZI | According to the experimental results in Table 4, we still have a high OoDWSR after model extraction. Could you explain why the model extraction methods using i.i.d data can also extract the watermarks that are generated using the OoD data? | SAFE AND ROBUST WATERMARK INJECTION WITH A SINGLE OoD IMAGE
Shuyang Yu1, Junyuan Hong1,2, Haobo Zhang1, Haotao Wang2, Zhaoyang Wang2 and Jiayu Zhou1
1Department of Computer Science and Engineering, Michigan State University
2Department of Electrical and Computer Engineering, University of Texas at Austin
{yushuyan,hon... |
86zAUE80pP | The hard cutoff based on hyperparameter k seems a bit weird to me. Towards what metric would the hyperparameter k be optimized for if it can't be equation 4 itself? This gets even weirder for me when the hard cutoff is then relaxed in equation 6. I don't get why it was ever introduced to begin with. | CPPO: Continual Learning for Reinforcement Learning with Human Feedback
Han Zhang\textsuperscript{1,2}, Yu Lei\textsuperscript{2,*}, Lin Gui\textsuperscript{3}, Min Yang\textsuperscript{4}, Yulan He\textsuperscript{4}, Hui Wang\textsuperscript{2}, Ruifeng Xu\textsuperscript{1,2,5,*}
\textsuperscript{1} Harbin Institu... |
7bIpWYhCdu | To justify that FILI outperforms large language models (LLMs), the edit accuracy subject to some delta-distance is used, with δ denoting the number of changes the fixer makes to the incorrect program. It turns out this edit accuracy is inadequate and potentially biased, as it overlooks the semantic correctness of the p... | FILI: Syntax Repair By Learning From Own Mistakes
Anonymous authors
Paper under double-blind review
Abstract
Automatically fixing syntax errors in programs is a key challenge in Software Engineering community. Although, there are millions of programs on the web, both syntactically correct and incorrect, finding a la... |
T8Rf1CRbHQ | All the convergence rates in this paper are in terms of the weighted sum of squared norms, while for unbiased SA or TTSA, the last-iterate convergence is achievable. Is this an inevitable issue when there exist structured errors? Or how does the last iterate behave in this case? | ABSTRACT
Two-time-scale stochastic approximation is a recursive algorithm for solving a system of two equations. The method has found broad applications in many areas including machine learning and reinforcement learning. Recent works have revealed that single-time-scale stochastic approximation (especially its varian... |
FlEUIydMMh | Theorem 3.6 states that the DAG G is identifiable with the conditions that $M_{i}\perp M_{j}\iff i-j\notin E^{\mathcal{U}}$, which is problematic. Since a DAG identifiable means that every direction for every edge in the DAG will be identified, which is impossible without any assumptions under the existence of a latent... | Neuro-Causal Factor Analysis
Anonymous authors
Paper under double-blind review
Abstract
Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychol... |
xHmCdSArUC | Also, $\nu$ is currently set to some small value in the experiments, so $(1-\nu)^t$ decays significantly slower than $\binom{1/2}{t}$. Thus, I wonder if $\nu$ can be dropped to save some tuning effort. | CORRELATED NOISE PROVABLY BEATS INDEPENDENT NOISE FOR DIFFERENTIALLY PRIVATE LEARNING
Christopher A. Choquette-Choo* Krishnamurthy (Dj) Dvijotham* Krishna Pillutla*
Arun Ganesh Thomas Steinke Abhradeep Guha Thakurta
Google
ABSTRACT
Differentially private (DP) learning algorithms inject noise into the learning proces... |
KrtGfTGaGe | Perhaps related to this additional assumption, the future work section mentioned adaptations under relaxed assumptions, with a citation to (Lambrechts et al. 2022). It would help to elaborate on some of those potential relaxations. | THE WASSERSTEIN BELIEVER
LEARNING BELIEF UPDATES FOR PARTIALLY OBSERVABLE ENVIRONMENTS THROUGH RELIABLE LATENT SPACE MODELS
Raphael Avalos\textsuperscript{1}*\hspace{1em} Florent Delgrange\textsuperscript{1,2}*\hspace{1em} Ann Nowé\textsuperscript{1}†\hspace{1em} Guillermo A. Pérez\textsuperscript{2,3}†\hspace{1em} Di... |
RJDjSXNuAZ | The method is evaluated on only virus detection in electron microscopy images, where viruses do not overlap. Thus, the method may not generalize to object detection (e.g., cell or nuclei detection) in other microscopy imaging modalities, such as hematoxylin and eosin (H&E) or immunohistochemistry (IHC) stained brightfi... | Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images
Hannah Kniesel, Leon Sick, Tristan Payer, Tim Bergner, Kavitha Shaga Devan, Clarissa Read, Paul Walther, Timo Ropinski
Ulm University
Pedro Hermosilla
TU Vienna
Abstract
Current state-of-the-art methods for object de... |
hdCDVSPQ7v | What is the update frequency used in Shampoo when comparing wall-clock time? Is possible to use a second-order update interval for Shampoo such that it runs at a similar speed to Jorge to achieve the target accuracy? | Jorge: Approximate Preconditioning for GPU-Efficient Second-Order Optimization
Anonymous authors
Paper under double-blind review
Abstract
Despite their better convergence properties compared to first-order optimizers, second-order optimizers for deep learning have been less popular due to their significant computati... |
r2ve0q6cIO | I am not sure I understand why one would want only the inference to be distributed if the training is not. More precisely, if the graphs are so large that inference needs to be distributed, how was the model even trained? | Graph Neural Networks Gone Hogwild
Anonymous authors
Paper under double-blind review
Abstract
Graph neural networks (GNNs) constitute a dominant class of architectures for modeling graph-structured data. Message-passing GNNs in particular appear to be ideal for applications where distributed inference is desired, si... |
kE9bsfMgin | In the light that there is no or little correlation between intrinsic bias measures and bias observed in a downstream task, how do you think the analysis of bias in attention heads is useful for downstream tasks? | ABSTRACT
Transformer-based pretrained large language models (PLM) such as BERT and GPT have achieved remarkable success in NLP tasks. However, PLMs are prone to encoding stereotypical biases. Although a burgeoning literature has emerged on stereotypical bias mitigation in PLMs, such as work on debiasing gender and rac... |
2XwBIcywWM | Algorithm 1 indicates the use of $n$ samples for each domain. Could the authors provide guidance on how to effectively balance these samples across various domains to ensure a harmonized and representative dataset for each domain involved? | Learning Variational Neighbor Labels for Test-Time Domain Generalization
Anonymous authors
Paper under double-blind review
Abstract
This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed on unseen target domains. We follow the strict separation of s... |
JiTVtCUOpS | As a continuation of my previous question, the use of cross-correlation for identifying lead-lag relationships suggests a focus on linear associations. May I inquire if this suggests that the algorithm's applicability is confined to variables that share a linear relationship (e.g., $X_1 = X_2^2$, the cross-correlation ... | Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
Lifan Zhao
Shanghai Jiao Tong University
mogician233@sjtu.edu.cn
Yanyan Shen*
Shanghai Jiao Tong University
shenyy@sjtu.edu.cn
Abstract
Recently, channel-independent methods have achieved state-of-the-ar... |
lgmCGI2IpI | In experiments, baseline “FULL” is using the entire labeled set for training? And the proposed AL algorithm querying much less labels could often surpass “FULL” from Figure 3? If so, seems interesting to understand the reasons behind. E.g., Is it because AQOT is better at filtering out noise? | AN EFFICIENT QUERY STRATEGY FOR ACTIVE LEARNING VIA OPTIMAL TRANSPORT
Anonymous authors
Paper under double-blind review
ABSTRACT
Active Learning (AL) aims to reduce labeling costs by iteratively querying instances. Existing AL methods typically query instances based on either informativeness or representativeness. O... |
5Lp6qU9hzV | In Table 4, it's intriguing to observe that BERT-MPU performs better than Roberta-MPU on full text but worse on short text. The authors could provide further insights or hypotheses as to why this performance gap exists. This could help readers better understand the nuances of the approach and its applicability in diffe... | MULTISCALE POSITIVE-UNLABELED DETECTION OF AI-GENERATED TEXTS
Yuchuan Tian¹, Hanting Chen², Xutao Wang², Zheyuan Bai², Qinghua Zhang²,
Ruifeng Li⁴, Chao Xu¹, Yunhe Wang²∗
¹ National Key Lab of General AI, School of Intelligence Science and Technology, Peking University
² Huawei Noah’s Ark Lab ³ Huawei Group Finance ⁴... |
SZn1Ex72Lv | What specific issues or challenges in the field of Feed Forward Neural Networks (FFNNs) are you addressing with your proposed concept of block-operations and the Multiplexer? How does this concept enhance FFNNs, and what practical applications or benefits can be derived from it? | Block-operations: Creating an Inductive Bias to Route Data and Reuse Subnetworks
Anonymous authors
Paper under double-blind review
Abstract
Feed Forward Neural Networks (FNNs) often suffer from poor generalization due to their inability to effectively develop and reuse subnetworks for related tasks. Csordás et al. (... |
pbLjYjjWqd | Sec 4.4 observed that most clients suffer from the dominant class issues. However, what is the loss for local clients when they are not well fitting across different classes? Also, is dominate class are “easy samples”? | FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
Anonymous authors
Paper under double-blind review
Abstract
Pre-trained language models (PLM) have revolutionized the NLP landscape, achieving stellar performances across diverse tasks. These models, while benefiting from vast training data... |
H8CtXin7mZ | In the context of traditional approaches of multigrid, either solver or preconditioner, either geometric or algebraic, imposing (nonhomogeneous) Neumann BC can be challenging and is usually an ad-hoc business. | A NEURAL-PRECONDITIONED POISSON SOLVER FOR MIXED DIRICHLET AND NEUMANN BOUNDARY CONDITIONS
Anonymous authors
Paper under double-blind review
ABSTRACT
We introduce a neural-preconditioned iterative solver for Poisson equations with mixed boundary conditions. The Poisson equation is ubiquitous in scientific computing:... |
FhbZ1PQCaG | Is the memory not initialized throughout the entire training process? Clarifying this point could help readers better understand the novelty of this approach, as memory initialization is typically performed per episode (e.g., NTM). | THINK BEFORE YOU ACT: DECISION TRANSFORMERS WITH INTERNAL MEMORY
Anonymous authors
Paper under double-blind review
ABSTRACT
Decision transformer model-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and computation. We argue t... |
rKMQhP6iAv | On the probing experiment. Technically speaking, if your data split is 50/50 yet the F1 is only 65%, isn’t it unconvincing that we could decode persona before the answers being generated? Could you provide other metrics, like accuracy, which is more widely adopted in probing literature? | PERSONAS AS A WAY TO MODEL TRUTHFULNESS IN LANGUAGE MODELS
Anonymous authors
Paper under double-blind review
ABSTRACT
Large Language Models (LLMs) are trained on vast amounts of text from the internet, which contains both factual and misleading information about the world. Can language models discern truth from fals... |
ESq3U7z6FD | In Sec 4.3, why is using beam-size = branching factored referred to as exact search? Are all documents visited in this setting? Also, how does beam-size = 0.1*branching_factor ensure that we search up to 10% documents? My understanding is that beam-size=b means that we end up at b leaf nodes and then exhaustively rank ... | EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval
Anonymous authors
Paper under double-blind review
Abstract
Dense embedding-based retrieval is now the industry standard for semantic search and ranking problems, like obtaining relevant web documents for a given query. Such techniques use a... |
bUv5gJAAxH | The authors have not accounted for the potential for bias in their Z-score test. The variance of the ID (and therefore the significance of the Z-score) itself strongly depends on the dimensionality within which it is assessed. Setting a threshold for hypothesis testing that is uniform across all dimensions may not be a... | Relating Implicit Bias and Adversarial Attacks through Intrinsic Dimension
Anonymous authors
Paper under double-blind review
Abstract
Despite their impressive performance in classification, neural networks are known to be vulnerable to adversarial attacks. These attacks are small perturbations of the input data desi... |
sFJr7okOBi | I don't understand the overall evaluation setup. What does 'We randomly generate 1000 protein sequences from these models'. What metadata did you condition on? Was it 1000 different sets of metadata? How do you make this comparison fair when using models like ESM that don't have the ability to condition on metadata? | NL2ProGPT: TAMING LARGE LANGUAGE MODEL FOR CONVERSATIONAL PROTEIN DESIGN
Anonymous authors
Paper under double-blind review
ABSTRACT
Large Language Models (LLMs), like ChatGPT, excel in cross-modal tasks thanks to their powerful abilities in natural language comprehension, generalization, and reasoning. Meanwhile, th... |
DOerIFfUbs | In Tab. 3, could you explain why you useLLaVA-Bench which is often used to test the multimodal instruction ability? And why the UTA with G/14 model did not beat that of L/14 model but exceeded in conversation and reasoning by a large margin? | Enhancing Vision-Language Model with Unmasked Token Alignment at Scale
Anonymous authors
Paper under double-blind review
Abstract
Contrastive pre-training on image-text pairs, exemplified by CLIP, becomes a standard technique for learning multi-modal visual-language representations. Although CLIP has demonstrated re... |
cmcD05NPKa | In claim U2, one can see that 21 is wrongly classified into $C_4$. As a result I wonder, for other bases that are product of larger primes, e.g., again, $2021=43\times 47$, can this phenomenon still be observe? | LEARNING THE GREATEST COMMON DIVISOR:
EXPLAINING TRANSFORMER PREDICTIONS
François Charton
Meta AI
fcharton@meta.com
ABSTRACT
The predictions of small transformers, trained to calculate the greatest common divisor (GCD) of two positive integers, can be fully characterized by looking at model inputs and outputs. As tr... |
AOpJ3vPNu8 | Descriptions in page 1 “However, optimally minimizing the individual task losses in the inner loop may not essentially lead to minimizing the average loss in the outer-loop…”. Based on my understanding, the goal of MAML is to find the meta initialization that minimizes the average inner task losses. So will the failure... | A Game Theoretic Approach to Meta-Learning
Nash Model-Agnostic Meta-Learning
Anonymous authors
Paper under double-blind review
Abstract
Meta-learning, or learning to learn, aims to develop algorithms that can quickly adapt to new tasks and environments. Model-agnostic meta-learning (MAML), proposed as a bi-level opt... |
b3LNKq6tfA | While the introduction of the RUID dataset (and its creation) are very interesting and useful, I argue if the randomness of the approach could generate many samples that are very hard to transform to code, thus impeding the improvement of performance at training time. | Learning UI-to-Code Reverse Generator using Visual Critic without Rendering
Anonymous authors
Paper under double-blind review
Abstract
Automated reverse engineering of HTML/CSS code from UI screenshots is an important yet challenging problem with broad applications in website development and design. In this paper, w... |
9k0krNzvlV | The motivation of this paper is there is a limitation of decoding-based watermarking, namely, replacing it with a normal decoder. Is the assumption practical? In practice, for an LLM API, how do we conduct such an operation? | ON THE LEARNABILITY OF WATERMARKS FOR LANGUAGE MODELS
Chenchen Gu, Xiang Lisa Li, Percy Liang, Tatsunori Hashimoto
Stanford University
{cygu, xlisali, thashim}@stanford.edu, pliang@cs.stanford.edu
ABSTRACT
Watermarking of language model outputs enables statistical detection of model-generated text, which can mitigat... |
SFCHv2G33F | Could you please provide more details on the secondary structure prediction (SSP) task? How many classes is this task composed of? Is there a separate prediction head for structure prediction on top of the cryptic pocket prediction head? Why and how is its weight in the objective function chosen to be 1? | Protein Language Models Enable Accurate Cryptic Ligand Binding Pocket Prediction
Anonymous authors
Paper under double-blind review
Abstract
Accurate prediction of protein-ligand binding pockets is a critical task in protein functional analysis and small molecule pharmaceutical design. However, the flexible and dynam... |
QuIiLSktO4 | For the synthetic noise a log-normal distribution is used. I believe you meant the parameters for this distribution are $\mu = 0$ and $\sigma \in [0,50]$. The mean of a log-normal distribution cannot be 0... | ALGORITHMS FOR CACHING AND MTS WITH REDUCED NUMBER OF PREDICTIONS∗
Karim Abdel Sadek
University of Amsterdam†
karim.abdel.sadek@student.uva.nl
Marek Eliáš
Department of Computing Sciences
Bocconi University
marek.elias@unibocconi.it
ABSTRACT
ML-augmented algorithms utilize predictions to achieve performance beyond ... |
qg5JENs0N4 | Just curious: regarding Lemma 4.1, do you have any comments or implications on the (sort of) bias term $\E_{p(h)} \left[ p^{\beta_h}_+ (s_{t+} \mid s) p^{\beta_h}_+ (s) \right] - p^{\beta}_+ (s_{t+} \mid s) p^{\beta}_+ (s)$ ? | CLOSING THE GAP BETWEEN TD LEARNING AND SUPERVISED LEARNING – A GENERALISATION POINT OF VIEW
Raj Ghugare\textsuperscript{1} Matthieu Geist\textsuperscript{2} Glen Berseth\textsuperscript{1,*} Benjamin Eysenbach\textsuperscript{3,*}
\textsuperscript{1}Mila, Université de Montréal \textsuperscript{2}Google DeepMind \te... |
xwZhyKynCB | Definition 12: For an abstract query graph G, a grounding is a function I that maps G into a query graph. Do you impose any restrictions on this mapping? For example, could two distinct nodes with the type | EFO$_k$-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
Anonymous authors
Paper under double-blind review
Abstract
To answer complex queries on knowledge graphs, logical reasoning over incomplete knowledge is required due to the open-world assumption. Learning-based methods are essential be... |
9OevMUdods | Data leaky may happen. Note that the questions and answers are annotated based on the text of previous public datasets. The pretraining data of evaluated LLMs is likely to contain the text. This may cause data leaky, and LLMs might have memorized/learned shortcuts to answer the question. It makes the evaluation results... | Towards Understanding Factual Knowledge of Large Language Models
Xuming Hu\textsuperscript{1,2*}, Junzhe Chen\textsuperscript{1*}, Xiaochuan Li\textsuperscript{1*}, Yufei Guo\textsuperscript{1}, Lijie Wen\textsuperscript{1†}, Philip S. Yu\textsuperscript{3}, Zhijiang Guo\textsuperscript{4†}
\textsuperscript{1} Tsingh... |
wlqkRFRkYc | The primary motivation of the paper is to address the limitations of two-dimensional image features in effectively representing global features within autonomous driving scenarios. The authors propose the use of BEV representations. The reviewer wonders why the authors did not explore 3D occupancy as a potential soluti... | BEV-CLIP: Multi-modal BEV Retrieval Methodology for Complex Scene in Autonomous Driving
Anonymous authors
Paper under double-blind review
Abstract
The demand for the retrieval of complex scene data in autonomous driving is increasing, especially as passenger vehicles have been equipped with the ability to navigate u... |
Ax9cPWDKkR | While using a post-hoc metric like Shapley or the proposed AI is beneficial in some circumstances, it should be mentioned that doing so requires extra (test time) compute. Algorithms like Q-MIX or VDN do not suffer from this issue, in the sense that they provide an estimation of contribution at any point in training. | EFFICIENTLY QUANTIFYING INDIVIDUAL AGENT IMPORTANCE IN COOPERATIVE MARL
Anonymous authors
Paper under double-blind review
ABSTRACT
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL). In cooperative MARL, team performance is typically inferred from ... |
22to0JZ4zh | Table 2 suggests that SSBM is more effective at preserving the marginal than minimizing the KL divergences. Do the authors have any insights or intuitions about why this is the case? It may be helpful to explain how this observation arises, as it is not explicitly implied by the algorithm itself. | SYMMETRIZED SCHRÖDINGER BRIDGE MATCHING
Anonymous authors
Paper under double-blind review
ABSTRACT
Schrödinger bridge (SB) has demonstrated numerous applications in probabilistic generative modeling. Finding the solution of probability paths aligns with entropy-regularized optimal transport that employs the Sinkhorn... |
ykW3hvy6DL | In theorem 6.4, the results show the existence of a subsequence of weights produced by NGD and GD that have a slow margin maximization rate. Is there a dataset where if one instead considers the sequence $w'_t$ where $$ w'_t \in \arg\min_{s \leq t} \| \frac{ w*(s) }{ \| w*{s}\| } - w^*} \| $$ has a much better margin m... | ACHIEVING Margin Maximization EXPONENTIALLY FAST via PROGRESSIVE NORM RESCALING
Anonymous authors
Paper under double-blind review
ABSTRACT
In this work, we investigate the margin-maximization bias exhibited by gradient-based algorithms in classifying linearly separable data. We present an in-depth analysis of the sp... |
IOrnCVIKIZ | Since the performance of LETI relies on the solution evaluator's implementation, could you elaborate on the potential biases that may arise from different evaluator designs? How might these biases impact the performance of LETI in optimizing towards certain metrics? | LETI: Learning to Generate from Textual Interactions
Anonymous authors
Paper under double-blind review
Abstract
Finetuning pre-trained language models (LMs) is essential for enhancing their capabilities and is a crucial phase in their lifecycles. Existing techniques commonly fine-tune on input-output pairs (e.g., in... |
sBSC0OXEQG | Eq. (2) presents the linear combination between auto- and hetero-associative memory using the hyperparameter $b$ and $(1-b). But this notation hints at convex combination $b \in [0, 1]$, where $b=0$ implies only hetero-associative memory and $b=1$ implies only auto-associative memory. **Why do we need a full linear com... | CORRELATED DENSE ASSOCIATIVE MEMORIES
Anonymous authors
Paper under double-blind review
ABSTRACT
We introduce a novel associative memory model named Correlated Dense Associative Memory (CDAM), which integrates both auto- and hetero-association in a unified framework for continuous-valued memory patterns. Employing a... |
EBUoTvVtMM | Framing existing attack terminology (“user inference”) as something new is confusing as there are already multiple works proposing the stronger threat model where either X texts of a user’s data or none were used to train the model (e.g., Song and Shmatikov). To the best of my understanding, it seems that user-level + ... | USER INFERENCE ATTACKS ON LARGE LANGUAGE MODELS
Anonymous authors
Paper under double-blind review
ABSTRACT
Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications. In this paper, we study the privacy implications of fine-tuning LLMs on user data. ... |
JuyFppXzh2 | The authors focus on short text. While it is widely used across the industry, it will be good to demonstrate why their approach is better for short text when compared to other approaches. In a sense what makes the approach more suited for short-text? | GANDALF: LEARNING LABEL CORRELATIONS IN EXTREME MULTI-LABEL CLASSIFICATION VIA LABEL FEATURES
Anonymous authors
Paper under double-blind review
ABSTRACT
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label c... |
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