Publications

Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning

Casey Meehan

Kamalika Chaudhuri

Chuan Guo

Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural image… (see more)s with one another. However, when taken to the extreme, SSL models can unintendedly memorize specific parts in individual training samples rather than learning semantically meaningful associations. In this work, we perform a systematic study of the unintended memorization of image-specific information in SSL models -- which we refer to as déjà vu memorization. Concretely, we show that given the trained model and a crop of a training image containing only the background (e.g., water, sky, grass), it is possible to infer the foreground object with high accuracy or even visually reconstruct it. Furthermore, we show that déjà vu memorization is common to different SSL algorithms, is exacerbated by certain design choices, and cannot be detected by conventional techniques for evaluating representation quality. Our study of déjà vu memorization reveals previously unknown privacy risks in SSL models, as well as suggests potential practical mitigation strategies.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

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… (see more)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.

2023-12-11

Conference on Neural Information Processing Systems (Accept (Poster))

doi.org

openreview.net

The Impact of Positional Encoding on Length Generalization in Transformers

Amirhossein Kazemnejad

Inkit Padhi

Karthikeyan Natesan Ramamurthy

Payel Das

Siva Reddy

Length generalization, the ability to generalize from small training context sizes to larger ones, is a critical challenge in the developmen… (see more)t of Transformer-based language models. Positional encoding (PE) has been identified as a major factor influencing length generalization, but the exact impact of different PE schemes on extrapolation in downstream tasks remains unclear. In this paper, we conduct a systematic empirical study comparing the length generalization performance of decoder-only Transformers with five different position encoding approaches including Absolute Position Embedding (APE), T5's Relative PE, ALiBi, and Rotary, in addition to Transformers without positional encoding (NoPE). Our evaluation encompasses a battery of reasoning and mathematical tasks. Our findings reveal that the most commonly used positional encoding methods, such as ALiBi, Rotary, and APE, are not well suited for length generalization in downstream tasks. More importantly, NoPE outperforms other explicit positional encoding methods while requiring no additional computation. We theoretically demonstrate that NoPE can represent both absolute and relative PEs, but when trained with SGD, it mostly resembles T5's relative PE attention patterns. Finally, we find that scratchpad is not always helpful to solve length generalization and its format highly impacts the model's performance. Overall, our work suggests that explicit position embeddings are not essential for decoder-only Transformers to generalize well to longer sequences.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

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openreview.net

Thinker: Learning to Plan and Act

Stephen Chung

Ivan Anokhin

David Krueger

We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a le… (see more)arned world model. The Thinker algorithm wraps the environment with a world model and introduces new actions designed for interacting with the world model. These model-interaction actions enable agents to perform planning by proposing alternative plans to the world model before selecting a final action to execute in the environment. This approach eliminates the need for handcrafted planning algorithms by enabling the agent to learn how to plan autonomously and allows for easy interpretation of the agent's plan with visualization. We demonstrate the algorithm's effectiveness through experimental results in the game of Sokoban and the Atari 2600 benchmark, where the Thinker algorithm achieves state-of-the-art performance and competitive results, respectively. Visualizations of agents trained with the Thinker algorithm demonstrate that they have learned to plan effectively with the world model to select better actions. Thinker is the first work showing that an RL agent can learn to plan with a learned world model in complex environments.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

A Unified, Scalable Framework for Neural Population Decoding

Mehdi Azabou

Vinam Arora

Venkataramana Ganesh

Ximeng Mao

Santosh Nachimuthu

Michael J. Mendelson

Blake Richards

Matthew G. Perich

Guillaume Lajoie

Eva L. Dyer

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size… (see more) and datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale.

2023-12-11

Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Versatile Energy-Based Probabilistic Models for High Energy Physics

Taoli Cheng

Aaron Courville

As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy functio… (see more)n. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In line with these advancements, we build a multi-purpose energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicative aspects, it can serve as a powerful parameterized event generator for physics simulation, a generic anomalous signal detector free from spurious correlations, and an augmented event classifier for particle identification.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability

Sitao Luan

Chenqing Hua

Minkai Xu

Qincheng Lu

Jiaqi Zhu

Xiao-Wen Chang

Jie Fu

Jure Leskovec

Doina Precup

Homophily principle, i.e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the perfo… (see more)rmance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks. Recent research suggests that, even in the absence of homophily, the advantage of GNNs still exists as long as nodes from the same class share similar neighborhood patterns. However, this argument only considers intra-class Node Distinguishability (ND) but neglects inter-class ND, which provides incomplete understanding of homophily on GNNs. In this paper, we first demonstrate such deficiency with examples and argue that an ideal situation for ND is to have smaller intra-class ND than inter-class ND. To formulate this idea and study ND deeply, we propose Contextual Stochastic Block Model for Homophily (CSBM-H) and define two metrics, Probabilistic Bayes Error (PBE) and negative generalized Jeffreys divergence, to quantify ND. With the metrics, we visualize and analyze how graph filters, node degree distributions and class variances influence ND, and investigate the combined effect of intra- and inter-class ND. Besides, we discovered the mid-homophily pitfall, which occurs widely in graph datasets. Furthermore, we verified that, in real-work tasks, the superiority of GNNs is indeed closely related to both intra- and inter-class ND regardless of homophily levels. Grounded in this observation, we propose a new hypothesis-testing based performance metric beyond homophily, which is non-linear, feature-based and can provide statistical threshold value for GNNs' the superiority. Experiments indicate that it is significantly more effective than the existing homophily metrics on revealing the advantage and disadvantage of graph-aware modes on both synthetic and benchmark real-world datasets.

2023-12-11

Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment

Tianwei Ni

Michel Ma

Benjamin Eysenbach

Pierre-Luc Bacon

Reinforcement learning (RL) algorithms face two distinct challenges: learning effective representations of past and present observations, an… (see more)d determining how actions influence future returns. Both challenges involve modeling long-term dependencies. The Transformer architecture has been very successful to solve problems that involve long-term dependencies, including in the RL domain. However, the underlying reason for the strong performance of Transformer-based RL methods remains unclear: is it because they learn effective memory, or because they perform effective credit assignment? After introducing formal definitions of memory length and credit assignment length, we design simple configurable tasks to measure these distinct quantities. Our empirical results reveal that Transformers can enhance the memory capability of RL algorithms, scaling up to tasks that require memorizing observations

2023-12-11

Conference on Neural Information Processing Systems (Accept (oral))

doi.org

openreview.net

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Avi Singh

John D Co-Reyes

Rishabh Agarwal

Ankesh Anand

Piyush Patil

Xavier Garcia

Peter J. Liu

James Harrison

Jaehoon Lee

Kelvin Xu

Aaron T Parisi

Abhishek Kumar

A. Alemi

Alex Rizkowsky

Azade Nova

Ben Adlam

Bernd Bohnet

Hanie Sedghi

Gamaleldin Fathy Elsayed

Igor Mordatch … (see 21 more)

Isabelle Simpson

Izzeddin Gur

Jasper Snoek

Jeffrey Pennington

Jiri Hron

Kathleen Kenealy

Kevin Swersky

Kshiteej Mahajan

Laura Culp

Lechao Xiao

Maxwell Bileschi

Noah Constant

Roman Novak

Rosanne Liu

Tris Brian Warkentin

Yundi Qian

Ethan Dyer

Behnam Neyshabur

Jascha Sohl-Dickstein

Yamini Bansal

Noah Fiedel

Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often lim… (see more)ited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness. To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST

2023-12-10

ArXiv (preprint)

doi.org

arxiv.org

Efficient Graphics Representation with Differentiable Indirection

Sayantan Datta

Carl Marshall

Zhao Dong

Zhengqin Li

D. Nowrouzezahrai

We introduce differentiable indirection – a novel learned primitive that employs differentiable multi-scale lookup tables as an effective … (see more)substitute for traditional compute and data operations across the graphics pipeline. We demonstrate its flexibility on a number of graphics tasks, i.e., geometric and image representation, texture mapping, shading, and radiance field representation. In all cases, differentiable indirection seamlessly integrates into existing architectures, trains rapidly, and yields both versatile and efficient results.

2023-12-10

SIGGRAPH Asia 2023 Conference Papers (published)

doi.org

arxiv.org

Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models

Arman Maesumi

Paul Guerrero

Noam Aigerman

Vladimir Kim

Matthew Fisher

Siddhartha Chaudhuri

Daniel Ritchie