Publications

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

Shengchao Liu

Weitao Du

Yanjing Li

Zhuoxinran Li

Zhiling Zheng

Chenru Duan

Zhiming Ma

Omar Yaghi

Anima Anandkumar

Christian Borgs

Jennifer Chayes

Hongyu Guo

Jian Tang

Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific comm… (voir plus)unities, particularly in the domains of chemistry, biology, and material discovery. For these scientific problems, molecules serve as the fundamental building blocks, and machine learning has emerged as a highly effective and powerful tool for modeling their geometric structures. Nevertheless, due to the rapidly evolving process of the field and the knowledge gap between science (e.g., physics, chemistry, & biology) and machine learning communities, a benchmarking study on geometrical representation for such data has not been conducted. To address such an issue, in this paper, we first provide a unified view of the current symmetry-informed geometric methods, classifying them into three main categories: invariance, equivariance with spherical frame basis, and equivariance with vector frame basis. Then we propose a platform, coined Geom3D, which enables benchmarking the effectiveness of geometric strategies. Geom3D contains 16 advanced symmetry-informed geometric representation models and 14 geometric pretraining methods over 46 diverse datasets, including small molecules, proteins, and crystalline materials. We hope that Geom3D can, on the one hand, eliminate barriers for machine learning researchers interested in exploring scientific problems; and, on the other hand, provide valuable guidance for researchers in computational chemistry, structural biology, and materials science, aiding in the informed selection of representation techniques for specific applications.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

doi.org

openreview.net

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Shenyang Huang

Farimah Poursafaei

Jacob Danovitch

Matthias Fey

Weihua Hu

Emanuele Rossi

Jure Leskovec

Michael M. Bronstein

Guillaume Rabusseau

Reihaneh Rabbany

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and r… (voir plus)obust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://tgb.complexdatalab.com/.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

doi.org

openreview.net

Augmenting Transit Network Design Algorithms with Deep Learning

Andrew Holliday

Gregory Dudek

This paper considers the use of deep learning models to enhance optimization algorithms for transit network design. Transit network design i… (voir plus)s the problem of determining routes for transit vehicles that minimize travel time and operating costs, while achieving full service coverage. State-of-the-art meta-heuristic search algorithms give good results on this problem, but can be very time-consuming. In contrast, neural networks can learn sub-optimal but fast-to-compute heuristics based on large amounts of data. Combining these approaches, we develop a fast graph neural network model for transit planning, and use it to initialize state-of-the-art search algorithms. We show that this combination can improve the results of these algorithms on a variety of metrics by up to 17%, without increasing their run time; or they can match the quality of the original algorithms while reducing the computing time by up to a factor of 50.

2023-09-23

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) (publié)

doi.org

Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification

Muberra Ozmen

Joseph Cotnareanu

Mark J. Coates

Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains… (voir plus). Most existing approaches require an enormous amount of annotated data to learn a classifier and/or a set of well-defined constraints on the label space structure, such as hierarchical relations which may be complicated to provide as the number of labels increases. In this paper, we study the MLTC problem in annotation-free and scarce-annotation settings in which the magnitude of available supervision signals is linear to the number of labels. Our method follows three steps, (1) mapping input text into a set of preliminary label likelihoods by natural language inference using a pre-trained language model, (2) calculating a signed label dependency graph by label descriptions, and (3) updating the preliminary label likelihoods with message passing along the label dependency graph, driven with a collective loss function that injects the information of expected label frequency and average multi-label cardinality of predictions. The experiments show that the proposed framework achieves effective performance under low supervision settings with almost imperceptible computational and memory overheads added to the usage of pre-trained language model outperforming its initial performance by 70\% in terms of example-based F1 score.

2023-09-23

ArXiv (prépublication)

doi.org

arxiv.org

Disorganized Communication and Social Dysfunction in Schizophrenia: Emerging Concepts and Methods

Emmanuel Olarewaju

Guillaume Dumas

L. Palaniyappan

2023-09-22

Current Psychiatry Reports (publié)

doi.org

Autonomic nervous system modulation during self-induced non-ordinary states of consciousness

Victor Oswald

Audrey Vanhaudenhuyse

Jitka Annen

Charlotte Martial

Aminata Bicego

Floriane Rousseaux

Corine Sombrun

Yann Harel

Marie-Elisabeth Faymonville

Steven Laureys

Karim Jerbi CoCo Lab

Olivia Gosseries

2023-09-21

Scientific Reports (publié)

doi.org

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets (Ultra Large Dataset)

Dominique Beaini

Shenyang Huang

Joao Alex Cunha

Zhiyi Li

Gabriela Moisescu-Pareja

Oleksandr Dymov

Samuel Maddrell-Mander

Callum McLean

Ali Parviz

Luis T. Díaz Müller

Jama Hussein Mohamud

Michael Craig

Cristian Gabellini

Jian Tang … (voir 8 de plus)

Christopher G. Morris

Mirco Ravanelli

Guy Wolf

Prudencio Tossou

Hadrien Mary

Błażej Banaszewski

Chad Martin

Dominic Masters

2023-09-21

Zenodo (inconnu)

doi.org

Are Diffusion Models Vision-And-Language Reasoners?

Christopher Pal

Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, unlik… (voir plus)e discriminative vision-and-language models, it is a non-trivial task to subject these diffusion-based generative models to automatic fine-grained quantitative evaluation of high-level phenomena such as compositionality. Towards this goal, we perform two innovations. First, we transform diffusion-based models (in our case, Stable Diffusion) for any image-text matching (ITM) task using a novel method called DiffusionITM. Second, we introduce the Generative-Discriminative Evaluation Benchmark (GDBench) benchmark with 7 complex vision-and-language tasks, bias evaluation and detailed analysis. We find that Stable Diffusion + DiffusionITM is competitive on many tasks and outperforms CLIP on compositional tasks like like CLEVR and Winoground. We further boost its compositional performance with a transfer setup by fine-tuning on MS-COCO while retaining generative capabilities. We also measure the stereotypical bias in diffusion models, and find that Stable Diffusion 2.1 is, for the most part, less biased than Stable Diffusion 1.5. Overall, our results point in an exciting direction bringing discriminative and generative model evaluation closer. We will release code and benchmark setup soon.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning

Charles Guille-escuret

Pau Rodríguez

David Vázquez

Ioannis Mitliagkas

Joao Monteiro

Handling out-of-distribution (OOD) samples has become a major stake in the real-world deployment of machine learning systems. This work expl… (voir plus)ores the use of self-supervised contrastive learning to the simultaneous detection of two types of OOD samples: unseen classes and adversarial perturbations. First, we pair self-supervised contrastive learning with the maximum mean discrepancy (MMD) two-sample test. This approach enables us to robustly test whether two independent sets of samples originate from the same distribution, and we demonstrate its effectiveness by discriminating between CIFAR-10 and CIFAR-10.1 with higher confidence than previous work. Motivated by this success, we introduce CADet (Contrastive Anomaly Detection), a novel method for OOD detection of single samples. CADet draws inspiration from MMD, but leverages the similarity between contrastive transformations of a same sample. CADet outperforms existing adversarial detection methods in identifying adversarially perturbed samples on ImageNet and achieves comparable performance to unseen label detection methods on two challenging benchmarks: ImageNet-O and iNaturalist. Significantly, CADet is fully self-supervised and requires neither labels for in-distribution samples nor access to OOD examples.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL

Chen Sun

Wannan Yang

Thomas Jiralerspong

Dane Malenfant

Benjamin Alsbury-Nealy

Yoshua Bengio

Blake Richards

In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward. T… (voir plus)hese critical steps are challenging to identify with traditional reinforcement learning (RL) methods that rely on the Bellman equation for credit assignment. Here, we present a new RL algorithm that uses offline contrastive learning to hone in on these critical steps. This algorithm, which we call Contrastive Retrospection (ConSpec), can be added to any existing RL algorithm. ConSpec learns a set of prototypes for the critical steps in a task by a novel contrastive loss and delivers an intrinsic reward when the current state matches one of the prototypes. The prototypes in ConSpec provide two key benefits for credit assignment: (i) They enable rapid identification of all the critical steps. (ii) They do so in a readily interpretable manner, enabling out-of-distribution generalization when sensory features are altered. Distinct from other contemporary RL approaches to credit assignment, ConSpec takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon (and ignoring other states) than it is to prospectively predict reward at every taken step. ConSpec greatly improves learning in a diverse set of RL tasks. The code is available at the link: https://github.com/sunchipsster1/ConSpec

2023-09-20

Neural Information Processing Systems (poster)

doi.org

openreview.net

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees

Sharan Vaswani

Amirreza Kazemi

Reza Babanezhad Harikandeh

Nicolas Roux

Actor-critic (AC) methods are widely used in reinforcement learning (RL) and benefit from the flexibility of using any policy gradient metho… (voir plus)d as the actor and value-based method as the critic. The critic is usually trained by minimizing the TD error, an objective that is potentially decorrelated with the true goal of achieving a high reward with the actor. We address this mismatch by designing a joint objective for training the actor and critic in a decision-aware fashion. We use the proposed objective to design a generic, AC algorithm that can easily handle any function approximation. We explicitly characterize the conditions under which the resulting algorithm guarantees monotonic policy improvement, regardless of the choice of the policy and critic parameterization. Instantiating the generic algorithm results in an actor that involves maximizing a sequence of surrogate functions (similar to TRPO, PPO) and a critic that involves minimizing a closely connected objective. Using simple bandit examples, we provably establish the benefit of the proposed critic objective over the standard squared error. Finally, we empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

A Definition of Continual Reinforcement Learning

David Abel

Andre Barreto

Benjamin Van Roy

Doina Precup

Hado van Hasselt

Satinder Singh

2023-09-20