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To solve this complex problem, the proposed method has to be broken down into many phases as shown in Section 2. That raises a question about the practicality: can the method be integrated as one to make it end-to-end. If not yet, what are the factors needed or what changes to enable that.
REINFORCEMENT LEARNING FOR NODE SELECTION IN BRANCH-AND-BOUND Anonymous authors Paper under double-blind review ABSTRACT A big challenge in branch and bound lies in identifying the optimal node within the search tree from which to proceed. Current state-of-the-art selectors utilize either hand-crafted ensembles that...
4VgBjsOC8k
The interpretation of what is the role of each ‘basis vectors’ DoG filter (such as “off-center” or “off-center cross”) is doing would help clarify what is the advantage for a CNN model to learn such filters.
UNVEILING THE UNSEEN: IDENTIFIABLE CLUSTERS IN TRAINED DEPTHWISE CONVOLUTIONAL KERNELS Zahra Babaiee TU Vienna & MIT zbabaiee@mit.edu Peyman M. Kiasari TU Vienna peyman.kiasari@tuwien.ac.at Daniela Rus MIT rus@mit.edu Radu Grosu TU Vienna radu.grosu@uwien.ac.at ABSTRACT Recent advances in depthwis...
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The analysis of Section 4.4 is an interesting way to see how the predicted latent shares information with the different image patches. What would this analysis give if you were to use the actual CLIP CLS token instead of the decoder’s prediction to compute the similarity scores? Would the scores look different for exam...
MINDGPT: INTERPRETING WHAT YOU SEE WITH NON-INVASIVE BRAIN RECORDINGS Anonymous authors Paper under double-blind review ABSTRACT Decoding of seen visual contents with non-invasive brain recordings has important scientific and practical values. Efforts have been made to recover the seen images from brain signals. How...
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Given that the proposed method relies on a non-parametric heuristic (PPR) for sampling, how interpretable are the final predictions and reasoning steps? Can the method provide insights into why a certain answer was chosen for a given query?
LESS IS MORE: ONE-SHOT-SUBGRAPH LINK PREDICTION ON LARGE-SCALE KNOWLEDGE GRAPHS Zhanke Zhou1 Yongqi Zhang2 Jiangchao Yao3 Quanming Yao4 Bo Han1† 1TMLR Group, Hong Kong Baptist University 2The Hong Kong University of Science and Technology (Guangzhou) 3CMIC, Shanghai Jiao Tong University 4Tsinghua University {cszkzhou,...
kxLMnvnZv0
Mountcastle et al. (1997) is given as a reference for the statement, “A cortical column contains only a few neurons (70−100)” but Mountcastle refers to this unit of organization as a minicolumn, and says that a column has many minicolumns.
ABSTRACT Designing ConvNet and exploring its design space is a highly challenging research area. In this paper, inspired by the structural organization of cortical modules in the biological visual cortex, we present a pragmatically designed ConvNet architecture, called CoMNet which is simplified yet powerful. The bio-...
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Three kinds of actor and critic inputs are introduced, namely, output state, hidden state and full state, which results in a critical design choice to be tuned for each domain. Although the authors provide some takeaways to select between them, it is not always true. For instance, output state policy is utilized in mem...
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...
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Could you please elaborate on the lack of results for PCBMs and ProbCBMs given my comments on the weaknesses indicating that they are in fact baselines that could be evaluated in the setups used in this paper?
ENERGY-BASED CONCEPT BOTTLENECK MODELS: UNIFYING PREDICTION, CONCEPT INTERVENTION, AND PROBABILISTIC INTERPRETATIONS Xinyue Xu\textsuperscript{1}, Yi Qin\textsuperscript{1}, Lu Mi\textsuperscript{2}, Hao Wang\textsuperscript{3†}, Xiaomeng Li\textsuperscript{1†} \textsuperscript{1}The Hong Kong University of Science an...
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It might not be practical to know all candidate tasks in advance, and just let the large model choose one. In the RL setup, the agent needs to explore the environment and finds out all candidate tasks.
OMNI: Open-endedness via Models of Human Notions of Interestingness Jenny Zhang\textsuperscript{1,2} Joel Lehman\textsuperscript{3} Kenneth Stanley\textsuperscript{4} Jeff Clune\textsuperscript{1,2,5} \textsuperscript{1}Department of Computer Science, University of British Columbia \textsuperscript{2}Vector Institute ...
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Since the authors claim that they use an optimizer for each code, and the Round-robin Optimization method optimizes each code in turn, what is the time efficiency of this method compared with the baselines?
3D-GOI: 3D GAN Omni-Inversion for Multi-Faceted and Multi-Object Editing Anonymous authors Paper under double-blind review Abstract The current GAN inversion methods typically can only edit the appearance and shape of a single object and background while overlooking spatial information. In this work, we propose a 3D...
rlCyHDzOjj
You claim that “The key difference between the TT-SVD and the TTT-SVD is the first works on unfolded matrices, while the latter deals with reshaped form of the underlying tensors, which are of order three”. Thus, can a decomposition deals with reshaped form of the underlying tensors with order greater than three achiev...
A NEW TENSOR NETWORK: TUBAL TENSOR TRAIN NETWORK AND ITS APPLICATIONS Anonymous authors Paper under double-blind review ABSTRACT This paper introduces the Tubal Tensor Train (TTT) decomposition, a novel tensor decomposition model that effectively mitigates the curse of dimensionality inherent in the Tensor Singular ...
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- The work is highly related to to Gauthier et al. 2022, but there is a lack of discussions on why this model is better than Gauthier et al. 2022 on a conceptual level, and how it is reflected in the experiment result.
COMPLEX-VALUED SCATTERING REPRESENTATIONS Anonymous authors Paper under double-blind review ABSTRACT Complex-valued deep learning has made significant progress with manifold geometry and group theory. It delivers leaner and better classifiers with novel complex-valued layer functions and network architectures, not o...
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I have concerns about the complexity of the INTENT-SIM algorithm. As it involves constructing graphs using external models and graph computations, this could significantly impact the computational efficiency of large language models.
Clarify When Necessary: Resolving Ambiguity with Language Models Anonymous authors Paper under double-blind review Abstract Resolving ambiguities through interaction is a hallmark of natural language, and modeling this behavior is a core challenge in crafting AI assistants. In this work, we study such behavior in LM...
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The node constraints vertices represent constraints added in addition to the root problem. They seem to be only one variable if they are constraints added by branching, as mentioned in 3.2 (the 4th line in the paragraph). In that case, each NC vertex will have only one edge connecting to one variable vertex. Is the tri...
Learning Node Selection via Tripartite Graph Representation in Mixed Integer Linear Programming Anonymous authors Paper under double-blind review Abstract Branch-and-bound methods are pivotal in solving Mixed Integer Linear Programs (MILPs), where the challenge of node selection arises, necessitating the prioritizat...
ExiBN1ZWJn
As the over-smoothing issue appears after only several Laplacian smoothing operations (i.e., node representations converge to identical after only several steps), it seems the value of time step $t$ can be small if we set the over-smoothing as the convergence state. Therefore, I wonder how to choose a proper $t$ to ens...
Denoising Graph Dissipation Model Improves Graph Representation Learning Anonymous authors Paper under double-blind review Abstract Graph-structured data are considered non-Euclidean as they provide superior representations of complex relations or interdependency. Many variants of graph neural networks (GNNs) have e...
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Given the database only provides 80 objects and the generated process is based on a retrieval manner, I wonder if the dataset is able to generate imagined objects or if daily objects look significantly different than the dataset assets.
SPADE: Training-Free Improvement of Spatial Fidelity in Text-to-Image Generation Anonymous authors Paper under double-blind review Abstract Text-to-Image (T2I) generation models have seen progressive improvements in their abilities to generate photo-realistic images. However, it has been demonstrated that they strug...
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Figure 4 is a little confusing. Different training objectives lead to different termination improvement occurrences, but is one of the three results better than the other two? It's not quite straight-forward to me.
LEARNING VARIABLE-LENGTH SKILLS THROUGH NOVELTY-BASED DECISION POINT IDENTIFICATION Anonymous authors Paper under double-blind review ABSTRACT Intelligent agents are able to make decisions based on different levels of granularity and duration. Recent advances in skill learning with data-driven behavior priors enable...
BtZ7vCt5QY
I guess, in my mind, it does very little good to say a method can deal with high dimensions without saying how high. There are two examples, one for 43 variables and another for 100 (genome reduced to this). Some may not consider this to be high-dimensional, so it would be better to say up front what dimension one hope...
Causal-StoNet: Causal Inference for High-Dimensional Complex Data Yaxin Fang Department of Statistics Purdue University West Lafayette, IN 47907, USA fang230@purdue.edu Faming Liang Department of Statistics Purdue University West Lafayette, IN 47907, USA fmliang@purdue.edu Abstract With the advancem...
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I was surprised that the position-specific embedding modifies the representation of a hyperredge in the aggregation for creating a new node representation as opposed to modifying the representation of the node in the aggregation for creating a hyperedge representation. Is there some benefit to your proposed approach co...
FULLY HYPERBOLIC REPRESENTATION LEARNING ON KNOWLEDGE HYPERGRAPH Anonymous authors Paper under double-blind review ABSTRACT Knowledge hypergraphs generalize knowledge graphs in terms of utilizing hyperedges to connect multiple entities and represent complicated relations within them. Existing methods either transfor...
IOrnCVIKIZ
As mentioned in Section 2.1, LETI assumes a code pre-trained LM which can give a decent, initial performance on code generation. I wonder if this is also a reason for the smaller LM benefiting less from LETI (they may be too weak initially)? It can be helpful if the authors could provide the initial good vs. bad instan...
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...
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In Section 4, what is the major challenge of extending the three-party communication protocol to a four-party communication protocol in Section 4.2? Why does one need to use the algebraic geometry code?
How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation Josh Alman Columbia University New York, NY, USA josh@cs.columbia.edu Zhao Song Adobe Research Seattle, WA, USA zsong@adobe.com Abstract In the classical transformer attention scheme, we are given th...
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The infinite horizon trajectories, according to the description in section 2, have random length sampled from the geometric distribution. Why geometric distribution is adopted here? The sampled number is still finite, so the cost in the time horizons greater than the sampled number is set to zero?
Better Imitation Learning in Discounted Linear MDP Anonymous authors Paper under double-blind review Abstract We present a new algorithm for imitation learning in infinite horizon linear MDPs dubbed ILARL which greatly improves the bound on the number of trajectories that the learner needs to sample from the environ...
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Although the authors claim that the proposed framework can quantitatively analyze the expressive power of different GNN variants based on the NED, the authors didn’t characterize the exact number of NED that exists for each NED class (share-point/strong/near strong/general NED). Thus, it is still hard to see the quanti...
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness Bohang Zhang\textsuperscript{1}∗† Jingchu Gai\textsuperscript{2}⋆ Yiheng Du\textsuperscript{3} Qiwei Ye\textsuperscript{4} Di He\textsuperscript{1,5}‡ Liwei Wang\textsuperscript{1,5}§ \textsuperscript{1}National Key Laboratory of General Artific...
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The derivation of transform coefficients is based on the assumption that $|| ildesymbol{a}||_1 = || ildesymbol{b}||_1$. In some UOT cases this assumption may not hold true. How the method behaves in such setting?
Solving (Partial) Unbalanced Optimal Transport via Transform Coefficients and Beyond Anonymous authors Paper under double-blind review Abstract Unbalanced Optimal Transport (UOT) has gained increasing attention due to its ability to relax marginal constraints, thereby expanding its application potential. Previous so...
PCm1oT8pZI
On page 5. ‘If the protected model shares a similar parameter distribution with the pre-trained model, the injected watermark could be easily erased by fine-tuning using clean i.i.d. data or adding random noise to parameters’. What is the pre-trained model?
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...
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I think there is a small notation issue in the second line of Equation 1; the T operator previously was applied to image-domain data in the un-numbered equation on page 2, but here is operating on k-space data.
Rigid Motion Compensated Compressed Sensing MRI with Untrained Neural Networks Anonymous authors Paper under double-blind review Abstract Deep neural networks trained end-to-end for accelerated magnetic resonance imaging give excellent performance. Typically, these networks are trained and evaluated under a setup wh...
BOm1RYdHHu
The difference between the norm and $w_i$ itself is significant, as the norm computation requires additional multiplication operations per gradient, which can seriously affect the modulus degree required for good gradient estimation and, thus, the overall runtime.
SAFHE: Defending Against Backdoor and Gradient Inversion Attacks in Federated Learning Anonymous authors Paper under double-blind review Abstract Federated learning (FL) is an increasingly popular approach in machine learning that enables a set of clients to jointly train a global model without ever sharing their pr...
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In the exploration scheme, where actions are ranked & selected based on value and then sampled based on uncertainty (or the other way around), you mention the possibility to adapt the trade-off preference between value and uncertainty - what do you mean by that?
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning Anonymous authors Paper under double-blind review Abstract Offline reinforcement learning (RL) provides a promising solution to learning an agent fully relying on a data-driven paradigm. However, constrained by the limited qua...
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Also, they assume that the source dataset is always available during model selection, which is generally not true if we consider the recent trend of foundation models where the source datasets are not available but only source models are released.
T-Measure: A Measure for Model Transferability Anonymous authors Paper under double-blind review Abstract A popular paradigm in AI modeling, including computer vision, natural language processing, and graph modeling, is applying a large pre-trained model that has been fine-tuned for a particular task on novel datase...
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The model-based evaluation is potentially beneficial, but on average, it incorrectly assigns or deducts points for 37% of the questions. This dependency on expert human evaluators limits the practicality of using this benchmark.
ABSTRACT Large Language Models (LLMs) have demonstrated remarkable performance on various quantitative reasoning and knowledge benchmarks. However, many of these benchmarks are losing utility as LLMs get increasingly high scores, despite not yet reaching expert performance in these domains. We introduce ARB, a novel b...
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Why $L_A$ does not include the accuracy of $f_{C\cup A\cup I \to Y}$? A(x) is input to $f_{C\cup A\cup I \to Y}$, but the gradient for the connection is stopped. Does not this have any negative impact on the whole design of the optimization procedure?
ADVERSARIAL LEARNING OF DECOMPOSED REPRESENTATIONS FOR TREATMENT EFFECT ESTIMATION Anonymous authors Paper under double-blind review ABSTRACT Estimating the Individual-level Treatment Effect (ITE) from observational data is an important issue both theoretically and practically. Including all the pre-treatment covari...
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In section 'EFFECTS OF VIEW CONDITIONS', suggesting that predictions are marginally affected within a 20-degree range, is perplexing. This statement seems to undermine the paper's central argument, suggesting that pose estimation and view conditions may not be as critical or even necessary. Could the authors elaborate ...
INTEGRATING OBJECT VIEW CONDITIONS FOR IMAGE SYNTHESIS Anonymous authors Paper under double-blind review ABSTRACT In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge. This paper introduces a pioneering framework that integrates viewpoint in...
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- The variables in the formula are very complex, and a lot of space was spent on deriving the objective function. At the same time, it is still difficult to understand why selectively aggregating them can make the global model approach local models more closely and affect the targeting of local data knowledge?
FedCDA: Federated Learning with Cross-Round Divergence-Aware Aggregation Haozhao Wang\textsuperscript{1}, Haoran Xu\textsuperscript{2}, Yichen Li\textsuperscript{3}, Yuan Xu\textsuperscript{1}, Ruixuan Li\textsuperscript{3,*}, Tianwei Zhang\textsuperscript{4} \textsuperscript{1}S-Lab, Nanyang Technological University...
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Authors claim that the performance of the model improves not with the number of parameters but with the state size. I am wondering if this is the case because the datasets considered such as MNIST are simple enough that having more parameters is no longer helpful rather than showing a general trend.
General-Purpose In-Context Learning by Meta-Learning Transformers Anonymous authors Paper under double-blind review Abstract Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims ...
eP6ZSy5uRj
The paper primarily focuses on the integration of a structure extractor with the ESM-2 model. However, with the availability of larger models like ESM2-3b, have there been empirical studies to assess the impact and advantages of the structure extractor? Specifically, as the scale of the PLM increases, does the benefit ...
Endowing Protein Language Models with Structural Knowledge Anonymous authors Paper under double-blind review Abstract Protein language models have shown strong performance in predicting function and structure across diverse tasks. These models undergo unsupervised pretraining on vast sequence databases to generate r...
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It seems this is not true from Figure 2 left: classes with larger hyperbolic radii have lower performance and are likely more difficult to recognize, and more complex. (BTW, there is no Fig (a) and (b), only left and right)
HYPERBOLIC ACTIVE LEARNING FOR SEMANTIC SEGMENTATION UNDER DOMAIN SHIFT Anonymous authors Paper under double-blind review ABSTRACT We introduce a hyperbolic neural network approach to pixel-level active learning for semantic segmentation, and propose a novel geometric interpretation of the hyperbolic geometry that a...
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On the LF-AOL-270K dataset, LEVER combined with ELIAS yields extremely high improvement on standard precision@k, especially at @5. These seem almost unrealistic when compared with scores of other methods. Are these numbers for sure correct? If yes, do the authors have any explanation for this result?
Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction Anirudh Buvanesh*, Rahul Chand*, Jatin Prakash, Bhawna Paliwal, Mudit Dhawan Neelabh Madan, Deepesh Hada, Vidit Jain, Sonu Mehta, Yashoteja Prabhu Manish Gupta, Ramachandran Ramjee, Manik Varma Microsoft {t-abuvanesh, t-rahalchand, t-japrak...
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Another issue reviewer is concerned about is the relationship between TAB and neuron dynamics. Does the exponential nonlinear operation of the SNN-based model on the time coefficient lead to an error in approximating a first-order linear ODE?
Spiking Neural Networks (SNNs) are attracting growing interest for their energy-efficient computing when implemented on neuromorphic hardware. However, directly training SNNs, even adopting batch normalization (BN), is highly challenging due to their non-differentiable activation function and the temporally delayed acc...
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It is quite hard to understand that Q-Tuning outperforms ProgPrompt (or Full prompts, MTL) under a long sequence of tasks. Since other methods have a bigger number of parameters (as the prompt size increases), I believe the performance also should be better. Can the author give an explanation or intuition behind these ...
Q-TUNING: CONTINUAL QUEUE-BASED PROMPT TUNING FOR LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT This paper introduces Q-tuning, a novel approach for continual prompt tuning that enables the lifelong learning of a pretrained language model on a sequence of tasks. For each new task, Q-tuni...
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Looking at the results from other papers (e.g., https://arxiv.org/pdf/2205.10683.pdf) applying ghost clipping to transformers, the ghost clipping has shown a much better memory effIciency. I do not quite understand the gap between Figure 3 and those results.
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data Anonymous authors Paper under double-blind review Abstract The Transformer has emerged as a versatile and effective architecture with broad applications. However, it still remains an open problem how to efficiently train a Transformer model of...
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Although the LLM is seemingly exploited in the theoretical analysis, what the theory really draws upon is actually a posterior inference oracle named LLM. Admittedly, a large transformer model pre-trained on carefully curated dataset may become such an oracle, but the assumption that a pre-trained large language model ...
REASON FOR FUTURE, ACT FOR NOW: A PRINCIPLED ARCHITECTURE FOR AUTONOMOUS LLM AGENTS Anonymous authors Paper under double-blind review ABSTRACT Large language models (LLMs) demonstrate impressive reasoning abilities, but translating reasoning into actions in the real world remains challenging. In particular, it is un...
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According to the abstract and introduction section, the goal of this work is to red-team from scratch. However, in step 2 of the proposed framework, we still need to choose a label set such that one of the labels represents undesirable outputs. This indicates the category of undesirable output is pre-defined, which is ...
EXPLORE, ESTABLISH, EXPLOIT: RED-TEAMING LANGUAGE MODELS FROM SCRATCH Anonymous authors Paper under double-blind review Warning: This paper contains AI-generated text that is offensive in nature. ABSTRACT Deploying large language models (LMs) can pose hazards from harmful outputs such as toxic or false text. Prior ...
RyUvzda8GH
The statement about iPC performing better on standard CNN than on AlexNet contradicts Table 1. According to Table 1, CNN accuracy is around 72\% whereas AlexNet accuracy is around 80\%. Therefore, iPC does not perform better on standard CNN than on AlexNet.
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks Tommaso Salvatori\textsuperscript{1,5,*}, Yuhang Song\textsuperscript{2,6,*†}, Yordan Yordanov\textsuperscript{3}, Beren Millidge\textsuperscript{2} Cornelius Emde\textsuperscript{3}, Zhenghua Xu\textsuperscript{4}, Lei Sha\textsuper...
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In the introduction, the authors mention two limitations in the existing LLMs and one of them is difficulty in incorporating fresh information, but how could the graph structure data solve this problem? I would encourage authors to elaborate more on this statement.
Talk Like a Graph: Encoding Graphs for Large Language Models Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi Google Research {baharef,halcrow,bperozzi}@google.com Abstract Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender sy...
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I'm curious if it's necessary in practice to satisfy all constraints. For instance, I wonder how performance would be affected if only the intent constraint was satisfied, or if only the METEOR constraint was satisfied (while still identifying proxy points through considering all RMs together)
Confronting Reward Model Overoptimization with Constrained RLHF Ted Moskovitz∗ Gatsby Unit, UCL Aaditya K. Singh Gatsby Unit, UCL DJ Strouse Google DeepMind Tuomas Sandholm Carnegie Mellon University† Ruslan Salakhutdinov Carnegie Mellon University Anca D. Dragan University of California, Berkeley Stephen McAlee...
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* Section 4.3: Why did you choose LIME and Anchor as baselines? There is no description of how they work or how they were trained. The advantage of PEACH over them is very high, which remains unexplained without given further context of how these methods work different.
PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure Anonymous authors Paper under double-blind review Abstract In this work, we propose a novel tree-based explanation technique, PEACH (Pretrained-embedding Explanation Across Contextual and Hierarchical Structure), that can explain ho...
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If learning representation of covariates inducing bias is unavoidable, how does the bias compare with bias due to finite-sample? e.g., How does it compare with the non-representation learning approach?
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation Valentyn Melnychuk, Dennis Frauen & Stefan Feuerriegel LMU Munich & Munich Center for Machine Learning Munich, Germany melnychuk@lmu.de Abstract State-of-the-art methods for conditional average treatment effect (CATE) estimation make w...
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The paper mentions 100 CT volumes for the LiTS dataset while the LiTS challenge website mentions that 130 CT scans are made available to the participants for training. Could you please explain the difference?
SYNTHETIC DATA AS VALIDATION Anonymous authors Paper under double-blind review ABSTRACT This study leverages synthetic data as a validation set to reduce overfitting and ease the selection of the best model in AI development. While synthetic data have been used for augmenting the training set, we find that synthetic...
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When multiple shallow networks are stacked together to build a deep neural network to produce high-level solution representations, why is the computational complexity of running this neural network akin to traditional evolutionary search operators?
Learning Deep Improvement Representation to Accelerate Evolutionary Optimization Anonymous authors Paper under double-blind review Abstract Evolutionary algorithms excel at versatile optimization for complex (e.g., multiobjective) problems but can be computationally expensive, especially in high-dimensional scenario...
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2. The proposed method primarily addresses the challenge of demonstration diversity stemming from different preferences among experts, but it does not explicitly tackle the diversity arising from multi-modality or stochastic behavior within the same preference category. For instance, some experts might have the same pr...
Learning From Multi-Expert Demonstrations: A Multi-Objective Inverse Reinforcement Learning Approach Anonymous authors Paper under double-blind review Abstract Imitation learning (IL) from a single expert’s demonstration has reached expert-level performance in many Mujoco environments. However, real-world environmen...
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It is misleading to claim that the proposed method **just** adds a non-trainable transformation over VDVAE. In contrast, the joint objective combined with Eq 13 in the appendix results in a full-fledged latent diffusion model that is jointly trained with the model.
Discouraging Posterior Collapse in Hierarchical Variational Autoencoders Using Context Anonymous authors Paper under double-blind review Abstract Hierarchical Variational Autoencoders (VAEs) are among the most popular likelihood-based generative models. There is a consensus that the top-down hierarchical VAEs allow ...
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The paper claims in the abstract and conclusion that the results show *greater* monosemanticity than other methods but I do not see such comparisons in the paper (it should be in section 5.1). Either I missed something or one of the main claims is not supported.
Sparse Autoencoders Find Highly Interpretable Features in Language Models Hoagy Cunningham∗12, Aidan Ewart∗13, Logan Riggs∗1, Robert Huben, Lee Sharkey4 1EleutherAI, 2MATS, 3University of Bristol, 4Apollo Research {hoagycunningham, aidanprattewart, logansmith5}@gmail.com Abstract One of the roadblocks to a better un...
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Considering the majority of the analysis assumes norms of all features are nearly same, due to the high dimensional limit, I do not see how this analysis can show the effects of feature normalization on non-contrastive learning.
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics Anonymous authors Paper under double-blind review Abstract Contrastive learning is a self-supervised representation learning framework, where two positive views generated through data augmentation are made similar by an attraction force in ...
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In the experimental evaluation, you compare AILBoost with existing off-policy AIL algorithms. Could you provide more insights into the reasons behind the superior performance of AILBoost compared to these baselines? What are the key factors or design choices in AILBoost that contribute to its improved performance? This...
ADVERSARIAL IMITATION LEARNING VIA BOOSTING Jonathan D. Chang Department of Computer Science Cornell University jdc396@cornell.edu Dhruv Sreenivas * Department of Computer Science Cornell University ds844@cornell.edu Yingbing Huang * Department of Electrical and Computer Engineering University of Ill...
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In the APPs benchmark, do you consider all problems from “codeforces”, “codechef” and “atcoder” ? Or is there some further filtering done after that ? If further filtering has been done, can you please clarify what procedure has been followed?
LLM-Assisted Code Cleaning For Training Accurate Code Generators Naman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E. Gonzalez, Koushik Sen & Ion Stoica University of California, Berkeley {naman_jain,tianjunz,weichiang,jegonzal,ksen,istoica}@berkeley.edu Abstract Natural language to code generation is an important a...
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The experiment and comparison in section 5.2 is disconnected from existing DFL work such as in Wilder et al. (2019) and Shah et al. (2022). Both of these papers also propose to use differentiable optimization for learning a model with the downstream task and use cvxpylayers. I would have found it more convincing to tak...
BPQP: A DIFFERENTIABLE CONVEX OPTIMIZATION FRAMEWORK FOR EFFICIENT END-TO-END LEARNING Anonymous authors Paper under double-blind review ABSTRACT Real-world decision-making processes often employ a two-stage approach, where a machine learning model first predicts key parameters, followed by a constrained convex opti...
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The crowdsourced experiment did not indicate the location of the neuron activation in each image. Based on the MILAN dataset it seems crucial to have this information to determine the semantic concept the neuron activates on. Why was this choice made?
DESCRIBE-AND-DISSECT: INTERPRETING NEURONS IN VISION NETWORKS WITH LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT In this paper, we propose Describe-and-Dissect, a novel method to describe the roles of hidden neurons in vision networks. Describe-and-Dissect utilizes recent advancements in...
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The paper relies heavily on empirical conclusions without providing a solid theoretical foundation for the proposed method. The authors should offer theoretical proof explaining why non-uniform strategies perform well, especially when prevailing LLM pruning strategies have contrasting conclusions.
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity Anonymous authors Paper under double-blind review Abstract Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge due to their colossal model size when it co...
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Which terms in Eq (8) and Eq (9) accounts for encouraging the coverage of the context space by experts? From the formulation, it seems to try to learn a set of policy each of which can solve the entire task space as much as possible. The learning of policies seem to be relatively independent and is it possible to learn...
REINFORCEMENT LEARNING OF DIVERSE SKILLS USING MIXTURE OF DEEP EXPERTS Anonymous authors Paper under double-blind review ABSTRACT Agents that can acquire diverse skills to solve the same task have a benefit over other agents. Unexpected environmental changes for example may prohibit executing a learned behavior such...
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Theorems 4.4 and 4.5 rely on Conjecture 4.3. However, this conjecture is not well stated in the main text. It would be also better to explain the difficulty of the proof and why this conjecture cannot be proved by previous results like Hu&Lu, (2023) and Ba et al. (2022).
A THEORY OF NON-LINEAR FEATURE LEARNING WITH ONE GRADIENT STEP IN TWO-LAYER NEURAL NETWORKS Anonymous authors Paper under double-blind review ABSTRACT Feature learning is thought to be one of the fundamental reasons for the success of deep neural networks. It is rigorously known that in two-layer fully-connected neu...
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From a cursory look, it seems that the MineCLIP baseline agent for tasks such as “hunt a cow” seems to severely underperform relative to the one from the original MineCLIP paper. Can you comment on this?
CLIP-GUIDED REINFORCEMENT LEARNING FOR OPEN- VOCABULARY TASKS Anonymous authors Paper under double-blind review ABSTRACT Open-vocabulary ability is crucial for an agent designed to follow natural language instructions. In this paper, we focus on developing an open-vocabulary agent through reinforcement learning. We ...
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In Figure 2, the authors mention extracting domain-invariant information from domain-specific features (Z^V). Given that these features reside in the encoded space (the output space of ResNet-18) if the training of ResNet-18 is indeed effective in extracting domain-invariant features, it raises a question: How can doma...
Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions Anonymous authors Paper under double-blind review Abstract Domain invariant learning aims to learn models that extract invariant features over various training domains, resulting in better generalization to unseen target domai...
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In the same paragraph, it is not obvious why a data-driven approach is necessarily a better alternative as it assumes access to a distribution of problem instances and requires offline training. Here it might be helpful to give a high-level explanation of why learning-based methods should work well.
DIFUSCO-LNS: DIFFUSION-GUIDED LARGE NEIGHBOURHOOD SEARCH FOR INTEGER LINEAR PROGRAMMING Anonymous authors Paper under double-blind review ABSTRACT Integer Linear Programming (ILP) is a powerful and flexible framework for modeling and solving a variety of combinatorial optimization problems. This paper introduces a n...
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To consider a similar objective, it needs to optimize $\max_u\min_{\alpha \geq 0} -\frac{1}{2} ||g_n -u||_2^2 + \alpha \langle u, g(\tilde{\lambda}^*)\rangle$. This gives $u^*=g_n + \alpha^* g(\tilde{\lambda}^*)$, where $\alpha^* = \arg\min_{\alpha} \frac{1}{2}||g_n + \alpha g(\tilde{\lambda}^*)||_2^2$. The optimal sol...
Enhanced Gradient Aligned Continual Learning via Pareto Optimization Anonymous authors Paper under double-blind review Abstract Catastrophic forgetting remains a core challenge in continual learning (CL), whereby the models struggle to retain previous knowledge when learning new tasks. While existing gradient-alignm...
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Could the author provide a detailed comparison between the information acquisition framework and the Bayesian Correlated Equilibrium (BCE) (Bergemann, D. 2016)? It seems to me that if the agents are not allowed to communicate with each other, these IC concepts are closely related and similar challenges occur for the le...
ONLINE INFORMATION ACQUISITION: HIRING MULTIPLE AGENTS Federico Cacciamani Politecnico di Milano federico.cacciamani@polimi.it Matteo Castiglioni Politecnico di Milano matteo.castiglioni@polimi.it Nicola Gatti Politecnico di Milano nicola.gatti@polimi.it ABSTRACT We investigate the mechanism design problem faced b...
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Compared to ProxSkip (Mishchenko et al. (2022)), the algorithm here requires finer structure information from the devices, i.e., individualized function smoothness parameters, while ProxSkip only requires a global smoothness parameter. And all clients are required to coordinate in advance to know the global information...
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity Anonymous authors Paper under double-blind review Abstract We study a class of distributed optimization algorithms that aim to alleviate high communication costs by allowing clients to perform multiple local gradient-type...
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When using a trained model for forecasting a single time series, how does inference look like in such a simple setting? Do attention models work well in such scenarios? In other words, do attention models produce better forecasts when a larger context in provided? The larger context could be in the form of multiple tim...
DAM: TOWARDS A FOUNDATION MODEL FOR TIME SERIES FORECASTING Luke Darlow, Qiwen Deng, Ahmed Hassan, Martin Asenov, Rajkarn Singh, Artjom Joosen, Adam Barker* Systems Infrastructure Research Edinburgh Research Centre Central Software Institute Huawei Edinburgh, UK sirlab@huawei.com Amos Storkey School of Informatics Un...
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In particular, as suggested above, in games, the distribution of $h$ is generally unknown and may be manipulated by an adversarial opponent, and in fact the critical thing is to be able to play well *regardless* of what distribution the opponent may choose.
ON SAMPLING INFORMATION SETS TO LEARN FROM IMPERFECT INFORMATION Anonymous authors Paper under double-blind review ABSTRACT In many real-world decision-making scenarios, agents are confronted with incomplete and imperfect information, requiring them to make choices based on limited knowledge. Imperfect-information g...
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This inquiry is vital because scenarios exist where the Normalized Dirichlet Energy may approach zero, yet the test accuracy remains high. For instance, consider a stochastic block matrix with two classes, where all nodes within one class map into a single representation, and the same occurs for the other class (to a d...
On Information Dropping and Oversmoothing in Graph Neural Networks Anonymous authors Paper under double-blind review Abstract Graph Neural Networks (GNNs) are widespread in graph representation learning. Random dropping approaches, notably DropEdge and DropMessage, claim to alleviate the key issues of overfitting an...
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Another question arises in this context. Considering that there could be multiple $f^*$ with identical testing performance, and they may not produce the same saliency maps, why does it matter if the saliency maps are influenced by the training data? Is there any guarantee that different $f^*\in\mathcal{F}$ with differe...
ON THE GENERALIZATION OF GRADIENT-BASED NEURAL NETWORK INTERPRETATIONS Anonymous authors Paper under double-blind review ABSTRACT Feature saliency maps are commonly used for interpreting neural network predictions. This approach to interpretability is often studied as a post-processing problem independent of trainin...
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In Tables 1-4, ChatGPT has consistently the best results for all methods, indicating that it is easier to distinguish from human text. But one would expect it to be harder, since it is a better model than the others. Do you have an explanation for this?
DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE OPTIMIZATION FOR MAXIMUM MEAN DISCREPANCY Shuhai Zhang12*, Yiliao Song3*, Jiahao Yang1, Yuanqing Li2††, Bo Han5†, Mingkui Tan14† South China University of Technology1 Pazhou Laboratory2 The University of Adelaide3 Key Laboratory of Big Data and Intelligent Ro...
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Could you share more insight on why organizing training data into monotonically increasing trajectories is able to mimic optimization paths? In particular, could you shed more light on the equation (13)?
Black-Box Gradient Matching for Reliable Offline Black-Box Optimization Anonymous authors Paper under double-blind review Abstract Offline design optimization problem arises in numerous science and engineering applications including materials engineering, where expensive online experimentation necessitates the use o...
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In the proof of Proposition 1, it is stated that $\tilde{A}$ and $\Delta \tilde{A}$ share the same eigenspace. Why is this true? It seems to be a very critical assumption that needs to be comprehensively justified and stated up front.
ADVective Diffusion Transformers for Topological Generalization in Graph Learning Anonymous authors Paper under double-blind review Abstract Graph diffusion equations are intimately related to graph neural networks (GNNs) and have recently attracted attention as a principled framework for analyzing GNN dynamics, for...
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Insufficient theoretical underpinning - Despite presenting a novel methodology, the paper falls short in providing an in-depth theoretical discussion to substantiate its claims. Specifically, it asserts that the model
Denoising Graph Dissipation Model Improves Graph Representation Learning Anonymous authors Paper under double-blind review Abstract Graph-structured data are considered non-Euclidean as they provide superior representations of complex relations or interdependency. Many variants of graph neural networks (GNNs) have e...
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I am curious about the limitations of the proposed method. Using LLM for self-improvement may suffer from performance degradation – Once the LLM generates some wrong correction, the overall performance may drop significantly. Have you noticed any domains experiencing performance declines when using LLMs to generate the...
Are Human-Generated Demonstrations Necessary for In-context Learning? Rui Li\textsuperscript{1}, Guoyin Wang\textsuperscript{2}, Jiwei Li\textsuperscript{3} \textsuperscript{1}University of Science and Technology of China \textsuperscript{2}Bytedance \textsuperscript{3}Zhejiang University Abstract Despite the promis...
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Does AFMO reduce to a BERT classifier when there is only textual data? If so, what could explain the fact that such simple model it is outperforming all the baselines on PolitiFact by a wide margin; don't you need to include a stronger baseline (e.g., dEFEND)? Or does AFMO still includes the feature selection step?
FAKE NEWS DETECTION VIA AN ADAPTIVE FEATURE MATCHING OPTIMIZATION FRAMEWORK Anonymous authors Paper under double-blind review ABSTRACT The rampant proliferation of fake news across online platforms has become a significant cause for concern, necessitating the creation of robust detection techniques. Within the confi...
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I found the explanation of the branching strategy to be quite confusing. For instance, the presentation of BaBSR heavily differs from the one from the original authors, which is based on computing coefficients that estimate the impact of splitting on the last layer bounds from the (Wong and Kolter 2018) paper. Could th...
Formal Verification for Neural Networks with General Nonlinearities via Branch-and-Bound Anonymous authors Paper under double-blind review Abstract Bound propagation with branch-and-bound (BaB) is so far among the most effective methods for neural network (NN) verification. However, existing works with BaB have most...
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The evaluation with the reported baselines then seems unfair since many baselines are trained to promote novelty and non-uniqueness (i.e., generating graphs that are not in the training set, and diverse).
RANDOM WALK DIFFUSION FOR GRAPH GENERATION Anonymous authors Paper under double-blind review ABSTRACT Graph generation addresses the problem of generating new graphs that have a data distribution similar to real-world graphs. Recently, the task of graph generation has gained increasing attention with applications ra...
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* Figure 2: what are the maximally possible values for the lower and upper bound in the task description? Is it possible that for minimal task information the intervals are so large that they always have the same value (and the network easily learns to ignore these constant values in its input)? Whereas for non-zero bu...
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...
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The design choices of the proposed approach are not explained and justified. Especially, the high-level world model learns to predict the future state and action just before the context switch, rather than the context and state right after the context switch. Predicting a low-level action sometime in the future sounds ...
Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics Christian Gumbsch\textsuperscript{1,2*}, Noor Sajid\textsuperscript{3}, Georg Martius\textsuperscript{2} & Martin V. Butz\textsuperscript{1} \textsuperscript{1} Neuro-Cognitive Modeling, University of Tübingen, Tübinge...
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So I am having trouble wrapping my mind around what the new conceptual contribution of this paper is: * In terms of techniques, the identification of the IOI subnetwork in GPT-2 medium reproduces an analysis of Wang et al. (2022) using their path patching method. The Colored Objects task network is identified running t...
Circuit Component Reuse Across Tasks in Transformer Language Models Jack Merullo Department of Computer Science Brown University jack_merullo@brown.edu Carsten Eickhoff School of Medicine University of Tübingen carsten.eickhoff@uni-tuebingen.de Ellie Pavlick Department of Computer Science Brown Unive...
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This is also observed in some visualizations in the supplementary materials where it does seem like the model improves the visual quality at the cost of diversity. I wonder if the authors could comment more on this?
**HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion** Xian Liu\(^1,2\)*, Jian Ren\(^1\)† Aliaksandr Siarohin\(^1\) Ivan Skorokhodov\(^1\) Yanyu Li\(^1\) Dahua Lin\(^2\) Xihui Liu\(^3\) Ziwei Liu\(^4\) Sergey Tulyakov\(^1\) \(^1\)Snap Inc. \(^2\)CUHK \(^3\)HKU \(^4\)NTU Project Page: https...
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It would be helpful if the manuscript could comment/discuss how the analysis of the transient in the deep linear network setting could inform the phenomemon of unimodal bias at convergence in practice.
A THEORY OF UNIMODAL BIAS IN MULTIMODAL LEARNING Anonymous authors Paper under double-blind review ABSTRACT Using multiple input streams simultaneously in training multimodal neural networks is intuitively advantageous, but practically challenging. A key challenge is unimodal bias, where a network overly relies on o...
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It is mentioned that the synthetic labels are initialized to be a fixed, balanced set, but the experiments have heterogeneous data. This sounds controversial and may deserve more explanation. When a class does not appear in a local dataset, what should we expect the synthetic data of that class to look like? How do tho...
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations Anonymous authors Paper under double-blind review Abstract This work proposes FedLAP-DP, a novel privacy-preserving approach for federated learning. Unlike previous linear point-wise gradient-sharing schemes, such as FedAvg, our form...
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The relationship between batch size and learning rate that I'm more familiar with (e.g., starting from Goyal et al. (2017)) is the linear scaling rule, but here it is shown to be squared, which has also been reported in the past (e.g., Krizhevsky (2014)). Can the authors elaborate on why this stability analysis leads t...
A Precise Characterization of SGD Stability Using Loss Surface Geometry Gregory Dexter\textsuperscript{1}, Borja Ocejo\textsuperscript{2}, Sathiya Keerthi\textsuperscript{2}, Aman Gupta\textsuperscript{2}, Ayan Acharya\textsuperscript{2} & Rajiv Khanna\textsuperscript{1} * \textsuperscript{1} Purdue University \textsu...
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In the Reacher environment, it's noted that the reward in the testing phase is significantly lower than during training. Could the author clarify whether there are differences in the parameters between Reacher's test and training environments?
NON-ERGODICITY IN REINFORCEMENT LEARNING: ROBUSTNESS VIA ERGODICITY TRANSFORMATIONS Anonymous authors Paper under double-blind review ABSTRACT Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in ...
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As experiments show (e.g. Fig.3 right), the object representation mostly is encoded by the tri-plane parameters, rather than the MLP. This raises a question of whether the MLP can be shared across data samples for efficiency?
Neural Processing of Tri-Plane Hybrid Neural Fields Adriano Cardace¹, Pierluigi Zama Ramirez¹, Francesco Ballerini¹, Allan Zhou², Samuele Salti¹, Luigi Di Stefano¹ ¹University of Bologna, ²Stanford University adriano.cardace2@unibo.it Abstract Driven by the appealing properties of neural fields for storing and commu...
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If I understand, what is sampled is always an equation, so of the form L = R. Then, L and R are sampled from a grammar with 18 operators, some unary, some binary, some terms with arity 0 (variables? constants?). These operators include 'integral' and 'derivative', and trigonometric functions, so the problems span algeb...
A SYMBOLIC FRAMEWORK FOR EVALUATING MATHEMATICAL REASONING WITH TRANSFORMERS Anonymous authors Paper under double-blind review ABSTRACT This paper proposes a methodology for generating synthetic mathematical derivations via a computer algebra system to evaluate the generalisability of Transformers in symbolic and qu...
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The article's experimental use of six datasets with relatively small differences between them, especially MSVD and MSR-VTT, raises concerns about the method's domain adaptation and continual learning capabilities.
Dynamic Adapter Merging for Continual Video Question-Answering Learning Anonymous authors Paper under double-blind review Abstract We present a parameter-efficient method for continual video question-answering (VidQA) learning. Our method, named DAM, uses Dynamic Adapter Merging to address the issues of (i) catastro...
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The current results on ResNet 50 suggest that the benefits of the proposed method in terms of accuracy v.s. compression trade-offs are marginal and do not include many other works such as [1,2] which all achieve more impressive results (without using second order importance estimation)
PRUNING NEURAL NETWORKS USING FISHLEG ESTIMATION Anonymous authors Paper under double-blind review ABSTRACT In many domains, the most successful AI models tend to be the largest, indeed often too large to be handled by AI players with limited computational resources. To mitigate this, a number of compression methods...
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The supplementary material provides videos for original dataset and generated samples. However, I find some samples where actions are not well-aligned with music beats at all, I doubt whether it is something performed by professional dancers. Also, from the generated samples, I saw that sometimes part of a body model p...
Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment Li Siyao$^1$ Tianpei Gu$^{2*}$ Zhengyu Lin$^3$ Zhitao Yang$^3$ Ziwei Liu$^1$ Henghui Ding$^1$ Lei Yang$^{3,4}$ Chen Change Loy$^1$ $^1$S-Lab, Nanyang Technological University $^2$Lexica $^3$SenseTime $^4$Shanghai AI Laboratory https:...
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However, ReForm-Eval includes many datasets that are trained in the evaluated VLM. This might incur two issues: (1) it is unfair to compare models that were trained by datasets evaluated in ReForm-Eval with models that have not been trained on any datasets in ReForm-Eval. (2) Ultimately, ReForm-Eval can only evaluate t...
ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks Anonymous authors Paper under double-blind review Abstract Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbo...
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What is the impact of self-curation iterations? Is there a value to performing multiple iterations? Did you also consider inference of the document collection on an improved reverse model from the data extracted (making the augmentation step also iterative)?
Self-Alignment with Instruction Backtranslation Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer Jason Weston & Mike Lewis Meta {xianl,jase,mikelewis}@meta.com Abstract We present a scalable method to build a high quality instruction following language model by automatically labelling human-...
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In the section on ‘Stealthiness of Backdoor Attacks’, Table 2 shows the qualitative results among different backdoor attack methods. However, the paper does not mention the number of samples for evaluation.
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection Jiawei Liang1, Siyuan Liang2*, Aishan Liu3, Xiaojun Jia4, Junhao Kuang1, Xiaochun Cao1* 1Sun Yat-Sen University 2National University of Singapore 3Beihang University 4Nanyang Technological University liangjw57@mail2.sysu.edu.cn pandaliang521@gm...
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As discussed in the introduction, two main limitations of existing evaluations are the limited type of perturbations, and testing data in a different distribution from training set. How well did this work improve these issues over previous evaluations? Are the perturbations more diverse? For the data distribution, ther...
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models Peiyan Zhang\textsuperscript{1}, Haoyang Liu\textsuperscript{2}, Chaozhuo Li\textsuperscript{3}\textsuperscript{*}, Xing Xie\textsuperscript{3}, Sunghun Kim\textsuperscript{1} and Haohan Wang\textsuperscript{2}\textsuperscript{*...
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Have you considered to what degree the scaling properties you found are a function of the underlying system (C elegans) vs of the networks you're training? I understand that brain activity data is hard to find, but perhaps it would be worthwhile to have a comparison baseline generated by a known dynamical system (e.g. ...
SCALING PROPERTIES FOR ARTIFICIAL NEURAL NETWORK MODELS OF THE C. elegans NERVOUS SYSTEM Anonymous authors Paper under double-blind review ABSTRACT The nematode worm C. elegans provides a unique opportunity for exploring intrinsic neural dynamics, given its transparency and well-characterized nervous system. This st...
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If I'm not mistaken, even if you sample exactly from DLD, the continuous Markov process doesn't exactly have the target distribution as the invariant distribution. Am I right? If so, this point should be highlighted.
STOCHASTIC GRADIENT DISCRETE LANGEVIN DYNAMICS Anonymous authors Paper under double-blind review ABSTRACT Sampling via Markov chain Monte Carlo can be inefficient when each evaluation of the energy function gradient depends on a large dataset. In continuous spaces, this challenge has been addressed by extending Lang...
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If they are directly comparable, why is VLM performance lower than LLM performance even when the VLMs have the scene metadata? They should be able to zero-out the contribution of the visual modality in each case. That should permit them to perform as well as language-only reasoning.
Dissecting Zero-Shot Visual Reasoning Capabilities in Vision and Language Models Anonymous authors Paper under double-blind review Abstract Vision-language models (VLMs) have shown impressive zero- and few-shot performance on real-world visual question answering (VQA) benchmarks, alluding to their capabilities as vi...
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FIGA also requires an external model to be available, such as gpt-3.5-turbo. This is a huge limitation. Moreover, this should be ablated against other strategies that would also use gpt-3.5-turbo, such as distillation strategies.
Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment Geyang Guo\textsuperscript{1}\thanks{Equal contribution.}, Ranchi Zhao\textsuperscript{1}\thanks{Equal contribution.}, Tianyi Tang\textsuperscript{1}, Wayne Xin Zhao\textsuperscript{1}\thanks{Corresponding author.}, Ji-Rong Wen\textsuperscript{1,2...
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Could the authors compare the data distribution assumptions made in this paper for finding the optimal guarantee of the solution, with the perturbation-resilient assumptions used in approximation design for $k$-clustering problems.
Universal Clustering Bounds Anonymous authors Paper under double-blind review Abstract This paper seamlessly integrates several fundamental learning tasks under the umbrella of subspace clustering, namely orthogonal nonnegative matrix factorization, and K-means clustering. Within this framework, we unveil a unified,...
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In my understanding, it is straightforward to make a variant of COTTA, which updates only BN layers. Noting that adapting BN layers is parameter-effective in TTA, it is also interesting to compare FETHER to the COTTA variant, in terms of parameter complexity and TTA/CTTA performance.
FEATHER: Lifelong Test-Time Adaptation with Lightweight Adapters Anonymous authors Paper under double-blind review Abstract Lifelong/continual test-time adaptation (TTA) refers to the problem where a pre-trained source domain model needs to be continually adapted at inference time to handle non-stationary test distr...