GenAI4ELab/papercli-papers huggingface dataset:
Neural Constraint Inference: Inferring Energy Constraints in Interacting Systems
Authors: Armand Comas; Yilun Du; Sandesh Ghimire; Christian Fernandez Lopez; Mario Sznaier; Joshua B. Tenenbaum; Octavia Camps
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Systems consisting of interacting agents are prevalent in the world, ranging from dynamical systems in physics to complex biological networks. To build systems which can interact robustly in the real world, it is thus important to be able to infer the precise interactions governing such systems.  Existing approaches typically discover such interactions by explicitly modeling the feedforward dynamics of the trajectories. In this work, we propose Neural Constraint Inference (NCI) model as an alternative approach to discover such interactions: it discovers a set of relational constraints, represented as energy functions, which when optimized reconstruct the original trajectory. We illustrate how NCI can faithfully predict future trajectory dynamics, achieving  more consistent long-rollouts than existing approaches. We show that the constraints discovered by NCI are disentangled and may be intermixed with constraints from other trajectories. Finally, we illustrate how those constraints enable the incorporation of external test-time constraints.
Clifford Neural Layers for PDE Modeling
Authors: Johannes Brandstetter; Rianne van den Berg; Max Welling; Jayesh K Gupta
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Partial differential equations (PDEs) see widespread use in sciences and engineering to describe simulation of physical processes as scalar and vector fields interacting and coevolving over time. Due to the computationally expensive nature of their standard solution methods, neural PDE surrogates have become an active research topic to accelerate these simulations. However, current methods do not explicitly take into account the relationship between different fields and their internal components, which are often correlated. Viewing the time evolution of such correlated fields through the lens of multivector fields allows us to overcome these limitations. Multivector fields consist of scalar, vector, as well as higher-order components, such as bivectors and trivectors. Their algebraic properties, such as multiplication, addition and other arithmetic operations can be described by Clifford algebras. To our knowledge, this paper presents the first usage of such multivector representations together with Clifford convolutions and Clifford Fourier transforms in the context of deep learning. The resulting Clifford neural layers are universally applicable and will find direct use in the areas of fluid dynamics, weather forecasting, and the modeling of physical systems in general. We empirically evaluate the benefit of Clifford neural layers by replacing convolution and Fourier operations in common neural PDE surrogates by their Clifford counterparts on 2D Navier-Stokes and weather modeling tasks, as well as 3D Maxwell equations. For similar parameter count, Clifford neural layers consistently improve generalization capabilities of the tested neural PDE surrogates.
Concept-level Debugging of Part-Prototype Networks
Authors: Andrea Bontempelli; Stefano Teso; Katya Tentori; Fausto Giunchiglia; Andrea Passerini
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets compute predictions based on similarity to class-specific part-prototypes learned to recognize parts of training examples, making it easy to faithfully determine what examples are responsible for any target prediction and why. However, like other models, they are prone to picking up confounders and shortcuts from the data, thus suffering from compromised prediction accuracy and limited generalization. We propose ProtoPDebug, an effective concept-level debugger for ProtoPNets in which a human supervisor, guided by the model’s explanations, supplies feedback in the form of what part-prototypes must be forgotten or kept, and the model is fine-tuned to align with this supervision. Our experimental evaluation shows that ProtoPDebug outperforms state-of-the-art debuggers for a fraction of the annotation cost. An online experiment with laypeople confirms the simplicity of the feedback requested to the users and the effectiveness of the collected feedback for learning confounder-free part-prototypes. ProtoPDebug is a promising tool for trustworthy interactive learning in critical applications, as suggested by a preliminary evaluation on a medical decision making task.
Faster Hyperparameter Search for GNNs via Calibrated Dataset Condensation
Authors: Mucong Ding; Xiaoyu Liu; Tahseen Rabbani; Teresa Ranadive; Tai-Ching Tuan; Furong Huang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Dataset condensation aims to reduce the computational cost of training multiple models on a large dataset by condensing the training dataset into a small synthetic set. State-of-the-art approaches rely on matching the model gradients for the real and synthetic data and have recently been applied to condense large-scale graphs for node classification tasks. Although dataset condensation may be efficient when training multiple models for hyperparameter optimization, there is no theoretical guarantee on the generalizability of the condensed data: data condensation often generalizes poorly across hyperparameters/architectures in practice, while we find and prove this overfitting is much more severe on graphs. In this paper, we consider a different condensation objective specifically geared towards hyperparameter search. We aim to generate the synthetic dataset so that the validation-performance rankings of the models, with different hyperparameters, on the condensed and original datasets are comparable. We propose a novel hyperparameter-calibrated dataset condensation algorithm, which obtains the synthetic validation data by matching the hyperparameter gradients computed via implicit differentiation and efficient inverse Hessian approximation. HCDC employs a supernet with differentiable hyperparameters, making it suitable for modeling GNNs with widely different convolution filters. Experiments demonstrate that the proposed framework effectively maintains the validation-performance rankings of GNNs and speeds up hyperparameter/architecture search on graphs.
Efficient Trojan Injection: 90% Attack Success Rate Using 0.04% Poisoned Samples
Authors: Pengfei Xia; Yueqi Zeng; Ziqiang Li; Wei Zhang; Bin Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: This study focuses on reducing the number of poisoned samples needed when backdooring an image classifier. We present Efficient Trojan Injection (ETI), a pipeline that significantly improves the poisoning efficiency through trigger design, sample selection, and the exploitation of individual consistency. Using ETI, two backdoored datasets, CIFAR-10-B0-20 and CIFAR-100-B0-30, are constructed and released, in which 0.04% (20/50,000) and 0.06% (30/50,000) of the train images are poisoned. Across 240 models with different network architectures and training hyperparameters, the average attack success rates on these two sets are 92.1% and 90.4%, respectively. These results indicate that it is feasible to inject a Trojan into an image classifier with only a few tens of poisoned samples, which is about an order of magnitude less than before.
Variational Autoencoders with Decremental Information Bottleneck for Disentanglement
Authors: Jiantao Wu; Shentong Mo; Muhammad Awais; Sara Atito; xingshen zhang; Lin Wang; Xiang Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity. Previous methods, spreading the conflict of disentanglement and reconstruction in time, will lose the constraint of disentanglement when expanding the information bottleneck, which causes the information diffusion problem. To tackle this issue, we present a novel decremental variational autoencoder with disentanglement-invariant transformations to spread the conflict on multiple latent spaces, termed DeVAE, for balancing disentanglement and reconstruction fidelity by decreasing the information bottleneck of diverse latent spaces gradually. Benefiting from the multiple latent spaces, DeVAE allows simultaneous optimization of multiple objectives to optimize reconstruction while keeping the constraint of disentanglement, avoiding information diffusion. DeVAE is also compatible with large models with high-dimension latent space. Experimental results on dSprites and Shapes3D that DeVAE achieves the best performance on both disentanglement and reconstruction.
Optimizing Data-Flow in Binary Neural Networks
Authors: Lorenzo Vorabbi; Davide Maltoni; Stefano Santi
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Binary Neural Networks (BNNs) can significantly accelerate the inference time of a neural network by replacing its expensive floating-point arithmetic with bit-wise operations. Most existing solutions, however, do not fully optimize data flow through the BNN layers, and intermediate conversions from 1 to 16/32 bits often further hinder efficiency. We propose a novel training scheme that can increase data flow and parallelism in the BNN pipeline; specifically, we introduce a clipping block that decreases the data-width from 32 bits to 8. Furthermore, we reduce the internal accumulator size of a binary layer, usually kept using 32-bit to prevent data overflow without losing accuracy. Additionally, we provide an optimization of the Batch Normalization layer that both reduces latency and simplifies deployment. Finally, we present an optimized implementation of the Binary Direct Convolution for ARM instruction sets. Our experiments show a consistent improvement of the inference speed (up to $1.77$ and $1.9 \times$ compared to two state-of-the-art BNNs frameworks) with no drop in accuracy for at least one full-precision model.
Learning to Register Unbalanced Point Pairs
Authors: Kanghee Lee; Junha Lee; Jaesik Park
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Point cloud registration methods can effectively handle large-scale, partially overlapping point cloud pairs. Despite its practicality, matching the unbalanced pairs in terms of spatial extent and density has been overlooked and rarely studied. We present a novel method, dubbed UPPNet, for Unbalanced Point cloud Pair registration. We propose to incorporate a hierarchical framework that effectively finds inlier correspondences by gradually reducing search space. The proposed method first predicts subregions within target point cloud that are likely to be overlapped with query. Then following super-point matching and fine-grained refinement modules predict accurate inlier correspondences between the target and query. Additional geometric constraints are applied to refine the correspondences that satisfy spatial compatibility. The proposed network can be trained in an end-to-end manner, predicting the accurate rigid transformation with a single forward pass. To validate the efficacy of the proposed method, we create a carefully designed benchmark, named KITTI-UPP dataset, by augmenting the KITTI odometry dataset. Extensive experiments reveal that the proposed method not only outperforms state-of-the-art point cloud registration methods by large margins on KITTI-UPP benchmark, but also achieves competitive results on the standard pairwise registration benchmark including 3DMatch, 3DLoMatch, ScanNet, and KITTI, thus showing the applicability of our method on various datasets. The source code and dataset will be publicly released.
Bi-Level Dynamic Parameter Sharing among Individuals and Teams for Promoting Collaborations in Multi-Agent Reinforcement Learning
Authors: Yan Liu; Ying Tiffany He; Fei Yu; Zhong Ming
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Parameter sharing has greatly contributed to the success of multi-agent reinforcement learning in recent years. However, most existing parameter sharing mechanisms are static, and parameters are indiscriminately shared among individuals, ignoring the dynamic environments and different roles of multiple agents. In addition, although a single-level selective parameter sharing mechanism can promote the diversity of strategies, it is hard to establish complementary and cooperative relationships between agents. To address these issues, we propose a bi-level dynamic parameter sharing mechanism among individuals and teams for promoting effective collaborations (BDPS). Specifically, at the individual level, we define virtual dynamic roles based on the long-term cumulative advantages of agents and share parameters among agents in the same role. At the team level, we combine agents of different virtual roles and share parameters of agents in the same group. Through the joint efforts of these two levels, we achieve a dynamic balance between the individuality and commonality of agents, enabling agents to learn more complex and complementary collaborative relationships. We evaluate BDPS on a challenging set of StarCraft II micromanagement tasks. The experimental results show that our method outperforms the current state-of-the-art baselines, and we demonstrate the reliability of our proposed structure through ablation experiments.
Learning to Count Everything: Transformer-based Trackers are Strong Baselines for Class Agnostic Counting
Authors: Tsung-Han Chou; Brian Schweitzer Wang; Han Ru Chen; Jun-Cheng Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Class agnostic counting (CAC) is a vision task which can be used to count the total occurrence number of any given reference objects in the query image. The task is usually formulated as density map estimation problem through similarity computation among few image samples of the reference object and the query image. In this paper, we show the the popular and effective similarity computation operation, bilinear similarity, actually share high resemblance with self-attention and cross-attention operations which are widely used in the transformer architecture. Inspired by this observation, since the formulation of visual object tracking task is similar to CAC, we show the advanced attention modules of transformer-based trackers are actually powerful matching tools for the CAC task. These modules allow to learn more distinct features to capture the shared patterns among the query and reference images. In addition, we propose a transformer-based class agnostic counting framework by adapting transformer-based trackers for CAC. We demonstrate the effectiveness of the proposed framework with two state-of-the-art transformer-based trackers, MixFormer and TransT, with extensive experiments and ablation studies. The proposed methods outperform other state-of-the-art methods on the challenging FSC-147 and CARPK datasets and achieve new state-of-the-art performances. The code will be publicly available upon acceptance.
ACQL: An Adaptive Conservative Q-Learning Framework for Offline Reinforcement Learning
Authors: Kun Wu; Yinuo Zhao; Zhiyuan Xu; Zhengping Che; Chengxiang Yin; Chi Harold Liu; Qinru Qiu; Feifei Feng; Jian Tang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Offline Reinforcement Learning (RL), which relies only on static datasets without additional interactions with the environment, provides an appealing alternative to learning a safe and promising control policy. Most existing offline RL methods did not consider relative data quality and only crudely constrained the distribution gap between the learned policy and the behavior policy in general. Moreover, these algorithms cannot adaptively control the conservative level in more fine-grained ways, like for each state-action pair, leading to a performance drop, especially over highly diversified datasets. In this paper, we propose an Adaptive Conservative Q-Learning (ACQL) framework that enables more flexible control over the conservative level of Q-function for offline RL. Specifically, we present two adaptive weight functions to shape the Q-values for collected and out-of-distribution data. Then we discuss different conditions under which the conservative level of the learned Q-function changes and define the monotonicity with respect to data quality and similarity. Motivated by the theoretical analysis, we propose a novel algorithm with the ACQL framework, using neural networks as the adaptive weight functions. To learn proper adaptive weight functions, we design surrogate losses incorporating the conditions for adjusting conservative levels and a contrastive loss to maintain the monotonicity of adaptive weight functions. We evaluate ACQL on the commonly-used D4RL benchmark and conduct extensive ablation studies to illustrate the effectiveness and state-of-the-art performance compared to existing offline DRL baselines.
Initial Value Problem Enhanced Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks
Authors: Xuanxi Zhang; Jihao Long; Wei Hu; Weinan E; Jiequn Han
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Closed-loop optimal control design for high-dimensional nonlinear systems has been a long-standing challenge. Traditional methods, such as solving the associated Hamilton-Jacobi-Bellman equation, suffer from the curse of dimensionality. Recent literature proposed a new promising approach based on supervised learning, by leveraging powerful open-loop optimal control solvers to generate training data and neural networks as efficient high-dimensional function approximators to fit the closed-loop optimal control. This approach successfully handles certain high-dimensional optimal control problems but still performs poorly on more challenging problems.  One of the crucial reasons for the failure is the so-called distribution mismatch phenomenon brought by the controlled dynamics. In this paper, we investigate this phenomenon and propose the initial value problem enhanced sampling method to mitigate this problem. We theoretically prove that this sampling strategy improves over the vanilla strategy on the classical linear-quadratic regulator by a factor proportional to the total time duration. We further numerically demonstrate that the proposed sampling strategy significantly improves the performance on tested control problems, including the optimal landing problem of a quadrotor and the optimal reaching problem of a 7 DoF manipulator.
Minimum Description Length Control
Authors: Ted Moskovitz; Ta-Chu Kao; Maneesh Sahani; Matthew Botvinick
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the agent learns the common structure among the tasks with which it is faced and then distills it into a simpler representation which facilitates faster convergence and generalization to new tasks. In doing so, MDL-C naturally balances adaptation to each task with epistemic uncertainty about the task distribution. We motivate MDL-C via formal connections between the MDL principle and Bayesian inference, derive theoretical performance guarantees, and demonstrate MDL-C's empirical effectiveness on both discrete and high-dimensional continuous control tasks.
EM-Network: Learning Better Latent Variable for Sequence-to-Sequence Models
Authors: Ji Won Yoon; SungHwan Ahn; Hyeonseung Lee; Minchan Kim; SeokMin Kim; Nam Soo Kim
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In a sequence-to-sequence (seq2seq) framework, the use of an unobserved latent variable, such as latent alignment and representation, is important to address the mismatch problem between the source input and target output sequences. Existing seq2seq literature typically learns the latent space by only consuming the source input, which might produce a sub-optimal latent variable for predicting the target. Extending an expectation-maximization (EM)-like algorithm, we introduce EM-Network that can yield the promising latent variable by leveraging the target sequence as the model's additional training input. The target input is used as guidance to provide the target-side context and reduce the candidates of the latent variable. The proposed framework is trained in a new self-distillation setup, allowing the original sequence model to benefit from the latent variable of the EM-Network. Specifically, the EM-Network's prediction serves as a soft label for training the inner sequence model, which only takes the source as input. We theoretically show that our training objective can be a lower bound for the log-likelihood of the sequence model and is justified from the EM perspective. We conduct comprehensive experiments on two sequence learning tasks: speech recognition and machine translation. Experimental results demonstrate that the EM-Network significantly advances the current state-of-the-art self-supervised learning approaches. It improves over the best prior work on speech recognition and establishes state-of-the-art performance on WMT'14 and IWSLT'14 datasets. Moreover, the proposed method even achieves considerable performance improvement for fully supervised learning.
Edge-Varying Fourier Graph Network for Multivariate Time Series Forecasting
Authors: Kun Yi; Qi Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable recent successful MTS methods are built with graph neural networks (GNNs) due to their essential capacity for relational modeling. However, previous work often used a static graph structure of time-series variables for modeling MTS failing to capture their ever-changing correlations over time. To this end, a fully-connected supra-graph connecting any two variables at any two timestamps is adaptively learned to capture the high-resolution variable dependencies via an efficient graph convolutional network. Specifically, we construct the Edge-Varying Fourier Graph Networks (EV-FGN) equipped with Fourier Graph Shift Operator (FGSO) which efficiently performs graph convolution in the frequency domain. As a result, a high-efficiency scale-free parameter learning scheme is derived for MTS analysis and forecasting according to the convolution theorem. Extensive experiments show that EV-FGN outperforms state-of-the-art methods on seven real-world MTS datasets.