GenAI4ELab/papercli-papers huggingface dataset:
Temporal Dynamic Quantization for Diffusion Models
Authors: Junhyuk So; Jungwon Lee; Daehyun Ahn; Hyungjun Kim; Eunhyeok Park
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility. However, its high storage and computation demands, resulting from the model size and iterative generation, hinder its use on mobile devices. Existing quantization techniques struggle to maintain performance even in 8-bit precision due to the diffusion model's unique property of temporal variation in activation. We introduce a novel quantization method that dynamically adjusts the quantization interval based on time step information, significantly improving output quality. Unlike conventional dynamic quantization techniques, our approach has no computational overhead during inference and is compatible with both post-training quantization (PTQ) and quantization-aware training (QAT). Our extensive experiments demonstrate substantial improvements in output quality with the quantized model across various configurations.
Self-Predictive Universal AI
Authors: Elliot Catt; Jordi Grau-Moya; Marcus Hutter; Matthew Aitchison; Tim Genewein; Gregoire Deletang; Li Kevin Wenliang; Joel Veness
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Reinforcement Learning (RL) algorithms typically utilize learning and/or planning techniques to derive effective policies. The integration of both approaches has proven to be highly successful in addressing complex sequential decision-making challenges, as evidenced by algorithms such as AlphaZero and MuZero, which consolidate the planning process into a parametric search-policy. AIXI, the most potent theoretical universal agent, leverages planning through comprehensive search as its primary means to find an optimal policy. Here we define an alternative universal agent, which we call Self-AIXI, that on the contrary to AIXI, maximally exploits learning to obtain good policies. It does so by self-predicting its own stream of action data, which is generated, similarly to other TD(0) agents, by taking an action maximization step over the current on-policy (universal mixture-policy) Q-value estimates. We prove that Self-AIXI converges to AIXI, and inherits a series of properties like maximal Legg-Hutter intelligence and the self-optimizing property.
FABind: Fast and Accurate Protein-Ligand Binding
Authors: Qizhi Pei; Kaiyuan Gao; Lijun Wu; Jinhua Zhu; Yingce Xia; Shufang Xie; Tao Qin; Kun He; Tie-Yan Liu; Rui Yan
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding.  FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at https://github.com/QizhiPei/FABind.
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
Authors: Julia Olkhovskaya; Jack Mayo; Tim van Erven; Gergely Neu; Chen-Yu Wei
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: We consider the adversarial linear contextual bandit setting, which
allows for the loss functions associated with each of $K$ arms to change
over time without restriction. Assuming the $d$-dimensional contexts are
drawn from a fixed known distribution, the worst-case expected regret
over the course of $T$ rounds is known to scale as $\tilde O(\sqrt{Kd
T})$. Under the additional assumption that the density of the contexts
is log-concave, we obtain a second-order bound of order $\tilde
O(K\sqrt{d V_T})$ in terms of the cumulative second moment of the
learner's losses $V_T$, and a closely related first-order bound of order
$\tilde O(K\sqrt{d L_T^*})$ in terms of the cumulative loss of the best
policy $L_T^*$. Since $V_T$ or $L_T^*$ may be significantly smaller than
$T$, these improve over the worst-case regret whenever the environment
is relatively benign. Our results are obtained using a truncated version
of the continuous exponential weights algorithm over the probability
simplex, which we analyse by exploiting a novel connection to the linear
bandit setting without contexts.
Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs
Authors: CHEN SHENGYUAN; YUNFENG CAI; Huang Fang; Xiao Huang; Mingming Sun
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Knowledge graph (KG) reasoning utilizes two primary techniques, i.e., rule-based and KG-embedding based. The former provides precise inferences, but inferring via concrete rules is not scalable. The latter enables efficient reasoning at the cost of ambiguous inference accuracy. Neuro-symbolic reasoning seeks to amalgamate the advantages of both techniques. The crux of this approach is replacing the predicted existence of all possible triples (i.e., truth scores inferred from rules) with a suitable approximation grounded in embedding representations. However, constructing an effective approximation of all possible triples' truth scores is a challenging task, because it needs to balance the tradeoff between accuracy and efficiency, while compatible with both the rule-based and KG-embedding models. To this end, we proposed a differentiable framework - DiffLogic. Instead of directly approximating all possible triples, we design a tailored filter to adaptively select essential triples based on the dynamic rules and weights. The truth scores assessed by KG-embedding are continuous, so we employ a continuous Markov logic network named probabilistic soft logic (PSL). It employs the truth scores of essential triples to assess the overall agreement among rules, weights, and observed triples. PSL enables end-to-end differentiable optimization, so we can alternately update embedding and weighted rules. On benchmark datasets, we empirically show that DiffLogic surpasses baselines in both effectiveness and efficiency.
Detecting hidden confounding in observational data using multiple environments
Authors: Rickard Karlsson; JH Krijthe
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: A common assumption in causal inference from observational data is that there is no hidden confounding. Yet it is, in general, impossible to verify the presence of hidden confounding factors from a single dataset. Under the assumption of independent causal mechanisms underlying the data-generating process, we demonstrate a way to detect unobserved confounders when having multiple observational datasets coming from different environments. We present a theory for testable conditional independencies that are only absent when there is hidden confounding and examine cases where we violate its assumptions: degenerate & dependent mechanisms, and faithfulness violations. Additionally, we propose a procedure to test these independencies and study its empirical finite-sample behavior using simulation studies and semi-synthetic data based on a real-world dataset. In most cases, the proposed procedure correctly predicts the presence of hidden confounding, particularly when the confounding bias is large.
Learning and processing the ordinal information of temporal sequences in recurrent neural circuits
Authors: Xiaolong Zou; Zhikun Chu; Qinghai Guo; Jie Cheng; Bo Hong; Si Wu; Yuanyuan Mi
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Temporal sequence processing is fundamental in brain cognitive functions. 
Experimental data has indicated that the representations of ordinal information and contents of temporal sequences are disentangled in the brain, but the neural mechanism underlying this disentanglement remains largely unclear. Here, we investigate how recurrent neural circuits learn to represent the abstract order structure of temporal sequences, and how this disentangled representation of order structure from that of contents facilitates the processing of temporal sequences. We show that with an appropriate learn protocol, a recurrent neural circuit can learn a set of tree-structured attractor states to encode the corresponding tree-structured orders of given temporal sequences. This abstract temporal order template can then be bound with different contents, allowing for flexible and robust temporal sequence processing. Using a transfer learning task, we demonstrate that the reuse of a temporal order template facilitates the acquisition of new temporal sequences of the same or similar ordinal structure. Using a key-word spotting task, we demonstrate that the attractor representation of order structure improves the robustness of temporal sequence discrimination, if the ordinal information is the key to differentiate different sequences. We hope this study gives us insights into the neural mechanism of representing the ordinal information of temporal sequences in the brain, and helps us to develop brain-inspired temporal sequence processing algorithms.
Multi Time Scale World Models
Authors: Vaisakh Shaj; Saleh GHOLAM ZADEH; Ozan Demir; Luiz Ricardo Douat; Gerhard Neumann
Venue: NeurIPS 2023 (NeurIPS 2023 spotlight)

Abstract: Intelligent agents use internal world models to reason and make predictions about different courses of their actions at many scales. Devising learning paradigms and architectures that allow machines to learn world models that operate at multiple levels of temporal abstractions while dealing with complex uncertainty predictions is a major technical hurdle. In this work, we propose a probabilistic formalism to learn multi-time scale world models which we call the Multi Time Scale State Space (MTS3) model. Our model uses a  computationally efficient inference scheme on multiple time scales for highly accurate long-horizon predictions and uncertainty estimates over several seconds into the future. Our experiments, which focus on action conditional long horizon future predictions, show that MTS3 outperforms recent methods on several system identification benchmarks including complex simulated and real-world dynamical systems. Code is available at this repository:
https://github.com/ALRhub/MTS3.
Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization
Authors: Yuxin Guo; Shijie Ma; Hu Su; Zhiqing Wang; Yuhao Zhao; Wei Zou; Siyang Sun; Yun Zheng
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Audio-Visual Source Localization (AVSL) aims to locate sounding objects within video frames given the paired audio clips. Existing methods predominantly rely on self-supervised contrastive learning of audio-visual correspondence. Without any bounding-box annotations, they struggle to achieve precise localization, especially for small objects, and suffer from blurry boundaries and false positives. Moreover, the naive semi-supervised method is poor in effectively utilizing the abundance of unlabeled audio-visual pairs. In this paper, we propose a novel Semi-Supervised Learning framework for AVSL, namely Dual Mean-Teacher (DMT), comprising two teacher-student structures to circumvent the confirmation bias issue. Specifically, two teachers, pre-trained on limited labeled data, are employed to filter out noisy samples via the consensus between their predictions, and then generate high-quality pseudo-labels by intersecting their confidence maps. The optimal utilization of both labeled and unlabeled data combined with this unbiased framework enable DMT to outperform current state-of-the-art methods by a large margin, with CIoU of $\textbf{90.4\%}$ and $\textbf{48.8\%}$ on Flickr-SoundNet and VGG-Sound Source, obtaining $\textbf{8.9\%}$ and $\textbf{9.6\%}$ improvements respectively, given only $3\%$ of data positional-annotated. We also extend our framework to some existing AVSL methods and consistently boost their performance. Our code is publicly available at https://github.com/gyx-gloria/DMT.
V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs
Authors: Senzhang Wang; Jun Yin; Chaozhuo Li; Xing Xie; Jianxin Wang
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: GNN explanation method aims to identify an explanatory subgraph which contains the most informative components of the full graph. However, a major limitation of existing GNN explainers is that they are not robust to the structurally corrupted graphs, e.g., graphs with noisy or adversarial edges. On the one hand, existing GNN explainers mostly explore explanations based on either the raw graph features or the learned latent representations, both of which can be easily corrupted. On the other hand, the corruptions in graphs are irregular in terms of the structural properties, e.g., the size or connectivity of graphs, which makes the rigorous constraints used by previous GNN explainers unfeasible. To address these issues, we propose a robust GNN explainer called V-InfoR. Specifically, a robust graph representation extractor, which takes insights of variational inference, is proposed to infer the latent distribution of graph representations. Instead of directly using the corrupted raw features or representations of each single graph, we sample the graph representations from the inferred distribution for the downstream explanation generator, which can effectively eliminate the minor corruption. We next formulate the explanation exploration as a graph information bottleneck (GIB) optimization problem. As a more general method that does not need any rigorous structural constraints, our GIB-based method can adaptively capture both the regularity and irregularity of the severely corrupted graphs for explanation. Extensive evaluations on both synthetic and real-world datasets indicate that V-InfoR significantly improves the GNN explanation performance for the structurally corrupted graphs. Code and dataset are available at https://anonymous.4open.science/r/V-InfoR-EF88
Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data
Authors: Praveen Venkatesh; Corbett Bennett; Sam Gale; Tamina K. Ramirez; Greggory Heller; Severine Durand; Shawn R Olsen; Stefan Mihalas
Venue: NeurIPS 2023 (NeurIPS 2023 spotlight)

Abstract: Recent advances in neuroscientific experimental techniques have enabled us to simultaneously record the activity of thousands of neurons across multiple brain regions. This has led to a growing need for computational tools capable of analyzing how task-relevant information is represented and communicated between several brain regions. Partial information decompositions (PIDs) have emerged as one such tool, quantifying how much unique, redundant and synergistic information two or more brain regions carry about a task-relevant message. However, computing PIDs is computationally challenging in practice, and statistical issues such as the bias and variance of estimates remain largely unexplored. In this paper, we propose a new method for efficiently computing and estimating a PID definition on multivariate Gaussian distributions. We show empirically that our method satisfies an intuitive additivity property, and recovers the ground truth in a battery of canonical examples, even at high dimensionality. We also propose and evaluate, for the first time, a method to correct the bias in PID estimates at finite sample sizes. Finally, we demonstrate that our Gaussian PID effectively characterizes inter-areal interactions in the mouse brain, revealing higher redundancy between visual areas when a stimulus is behaviorally relevant.
Prototypical Variational Autoencoder for 3D Few-shot Object Detection
Authors: Weiliang Tang; Biqi YANG; Xianzhi Li; Yun-Hui Liu; Pheng-Ann Heng; Chi-Wing Fu
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Few-Shot 3D Point Cloud Object Detection (FS3D) is a challenging task, aiming to detect 3D objects of novel classes using only limited annotated samples for training. Considering that the detection performance highly relies on the quality of the latent features, we design a VAE-based prototype learning scheme, named prototypical VAE (P-VAE), to learn a probabilistic latent space for enhancing the diversity and distinctiveness of the sampled features. The network encodes a multi-center GMM-like posterior, in which each distribution centers at a prototype. For regularization, P-VAE incorporates a reconstruction task to preserve geometric information. To adopt P-VAE for the detection framework, we formulate Geometric-informative Prototypical VAE (GP-VAE) to handle varying geometric components and Class-specific Prototypical VAE (CP-VAE) to handle varying object categories. In the first stage, we harness GP-VAE to aid feature extraction from the input scene. In the second stage, we cluster the geometric-informative features into per-instance features and use CP-VAE to refine each instance feature with category-level guidance. Experimental results show the top performance of our approach over the state of the arts on two FS3D benchmarks. Quantitative ablations and qualitative prototype analysis further demonstrate that our probabilistic modeling can significantly boost prototype learning for FS3D.
Model-Free Active Exploration in Reinforcement Learning
Authors: Alessio Russo; Alexandre Proutiere
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. We adopt an information-theoretical viewpoint and start from the  instance-specific lower bound of the number of samples that have to be collected to identify a nearly-optimal policy. Deriving this lower bound along with the optimal exploration strategy entails solving an intricate optimization problem and requires a model of the system. In turn, most existing sample optimal exploration algorithms rely on estimating the model. We derive an approximation of the instance-specific lower bound that only involves quantities that can be inferred using model-free approaches. Leveraging this approximation, we devise an ensemble-based model-free exploration strategy  applicable to both tabular and continuous Markov decision processes. Numerical results demonstrate that our strategy is able to identify efficient policies faster than state-of-the-art exploration approaches.
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
Authors: Emmeran Johnson; Ciara Pike-Burke; Patrick Rebeschini
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Policy Mirror Descent (PMD) is a general family of algorithms that covers a wide range of novel and fundamental methods in reinforcement learning. Motivated by the instability of policy iteration (PI) with inexact policy evaluation, unregularised PMD algorithmically regularises the policy improvement step of PI without regularising the objective function. With exact policy evaluation, PI is known to converge linearly with a rate given by the discount factor $\gamma$ of a Markov Decision Process. In this work, we bridge the gap between PI and PMD with exact policy evaluation and show that the dimension-free $\gamma$-rate of PI can be achieved by the general family of unregularised PMD algorithms under an adaptive step-size. We show that both the rate and step-size are unimprovable for PMD: we provide matching lower bounds that demonstrate that the $\gamma$-rate is optimal for PMD methods as well as PI and that the adaptive step-size is necessary to achieve it. Our work is the first to relate PMD to rate-optimality and step-size necessity. Our study of the convergence of PMD avoids the use of the performance difference lemma, which leads to a direct analysis of independent interest. We also extend the analysis to the inexact setting and establish the first dimension-optimal sample complexity for unregularised PMD under a generative model, improving upon the best-known result.
Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks
Authors: Jun Yin; Chaozhuo Li; Hao Yan; Jianxun Lian; Senzhang Wang
Venue: NeurIPS 2023 (NeurIPS 2023 poster)

Abstract: Intrinsic interpretable graph neural networks aim to provide transparent predictions by identifying the influential fraction of the input graph that guides the model prediction, i.e., the explanatory subgraph. However, current interpretable GNNs mostly are dataset-specific and hard to generalize to different graphs. A more generalizable GNN interpretation model which can effectively distill the universal structural patterns of different graphs is until-now unexplored. Motivated by the great success of recent pre-training techniques, we for the first time propose the Pre-training Interpretable Graph Neural Network ($\pi$-GNN) to distill the universal interpretability of GNNs by pre-training over synthetic graphs with ground-truth explanations. Specifically, we introduce a structural pattern learning module to extract diverse universal structure patterns and integrate them together to comprehensively represent the graphs of different types. Next, a hypergraph refining module is proposed to identify the explanatory subgraph by incorporating the universal structure patterns with local edge interactions. Finally, the task-specific predictor is cascaded with the pre-trained $\pi$-GNN model and fine-tuned over downstream tasks. Extensive experiments demonstrate that $\pi$-GNN significantly surpasses the leading interpretable GNN baselines with up to 9.98\% interpretation improvement and 16.06\% classification accuracy improvement. Meanwhile, $\pi$-GNN pre-trained on graph classification task also achieves the top-tier interpretation performance on node classification task, which further verifies its promising generalization performance among different downstream tasks. Our code and datasets are available at https://anonymous.4open.science/r/PI-GNN-F86C