Publications

Graph Neural Networks with Learnable Structural and Positional Representations

Vijay Prakash Dwivedi

Anh Tuan Luu

Thomas Laurent

Xavier Bresson

Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging … (voir plus)from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical positional information of nodes, which decreases the representation power of GNNs to distinguish e.g. isomorphic nodes and other graph symmetries. An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers. Possible graph PE are Laplacian eigenvectors. In this work, we propose to decouple structural and positional representations to make easy for the network to learn these two essential properties. We introduce a novel generic architecture which we call LSPE (Learnable Structural and Positional Encodings). We investigate several sparse and fully-connected (Transformer-like) GNNs, and observe a performance increase for molecular datasets, from 1.79% up to 64.14% when considering learnable PE for both GNN classes.

2022-04-24

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Latent Variable Sequential Set Transformers for Joint Multi-agent Motion Prediction

Jim Aldon D’Souza

Samira E. Kahou

Felix Heide

Christopher Pal

Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a r… (voir plus)epresentation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. We propose Latent Variable Sequential Set Transformers which are encoder-decoder architectures that generate scene-consistent multi-agent trajectories. We refer to these architectures as "AutoBots". The encoder is a stack of interleaved temporal and social multi-head self-attention (MHSA) modules which alternately perform equivariant processing across the temporal and social dimensions. The decoder employs learnable seed parameters in combination with temporal and social MHSA modules allowing it to perform inference over the entire future scene in a single forward pass efficiently. AutoBots can produce either the trajectory of one ego-agent or a distribution over the future trajectories for all agents in the scene. For the single-agent prediction case, our model achieves top results on the global nuScenes vehicle motion prediction leaderboard, and produces strong results on the Argoverse vehicle prediction challenge. In the multi-agent setting, we evaluate on the synthetic partition of TrajNet++ dataset to showcase the model's socially-consistent predictions. We also demonstrate our model on general sequences of sets and provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. A distinguishing feature of AutoBots is that all models are trainable on a single desktop GPU (1080 Ti) in under 48h.

2022-04-24

International Conference on Learning Representations (Accept (Spotlight))

doi.org

openreview.net

Learning to Guide and to Be Guided in the Architect-Builder Problem

Paul Barde

Tristan Karch

Derek Nowrouzezahrai

Clément Moulin-Frier

Christopher Pal

Pierre-Yves Oudeyer

We are interested in interactive agents that learn to coordinate, namely, a …

2022-04-24

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Memory Augmented Optimizers for Deep Learning

Paul-Aymeric McRae

Prasanna Parthasarathi

Mahmoud Assran

Sarath Chandar

Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradi… (voir plus)ents for efficient parameter updates. The aggregated history of gradients nudges the parameter updates in the right direction even when the gradients at any given step are not informative. Although the history of gradients summarized in meta-parameters or explicitly stored in memory has been shown effective in theory and practice, the question of whether

2022-04-24

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

Tong He

Lijun An

Pansheng Chen

Jianzhong Chen

Jiashi Feng

Danilo Bzdok

Avram J. Holmes

Simon B. Eickhoff

B. T. Thomas Yeo

We propose a simple framework—meta-matching—to translate predictive models from large-scale datasets to new unseen non-brain-imaging phe… (voir plus)notypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = −0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching. Individual-level prediction is critical for precision medicine, but many neuroimaging prediction studies are underpowered. Here the authors present a simple yet powerful approach that effectively translates predictive models from big to small data.

2022-04-24

Nature Neuroscience (publié)

doi.org

MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling

Yusong Wu

Ethan Manilow

Yi Deng

Rigel Swavely

Kyle Kastner

Tim Cooijmans

Aaron Courville

Cheng-Zhi Anna Huang

Jesse Engel

Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detai… (voir plus)led expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience.

2022-04-24

International Conference on Learning Representations (Accept (Oral))

doi.org

openreview.net

Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation

Yan Zhang

David W. Zhang

Simon Lacoste-Julien

Gertjan J. Burghouts

Cees G. M. Snoek

Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets. We show that set-equivar… (voir plus)iant functions cannot represent certain functions on multisets, so we introduce the more appropriate notion of multiset-equivariance. We identify that the existing Deep Set Prediction Network (DSPN) can be multiset-equivariant without being hindered by set-equivariance and improve it with approximate implicit differentiation, allowing for better optimization while being faster and saving memory. In a range of toy experiments, we show that the perspective of multiset-equivariance is beneficial and that our changes to DSPN achieve better results in most cases. On CLEVR object property prediction, we substantially improve over the state-of-the-art Slot Attention from 8% to 77% in one of the strictest evaluation metrics because of the benefits made possible by implicit differentiation.

2022-04-24

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Lucas Caccia

Tinne Tuytelaars

In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. E… (voir plus)xperience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes' representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks

2022-04-24

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

I NTRODUCING C OORDINATION IN C ONCURRENT R EIN - FORCEMENT L EARNING

Adrien Ali Taiga

Aaron Courville

Bellemare Marc-Emmanuel

Google Brain

Research on exploration in reinforcement learning has mostly focused on problems with a single agent interacting with an environment. Howeve… (voir plus)r many problems are better addressed by the concurrent reinforcement learning paradigm, where multiple agents operate in a common environment. Recent work has tackled the challenge of exploration in this particular setting (Dimakopoulou & Van Roy, 2018; Dimakopoulou et al., 2018). Nonetheless, they do not completely leverage the characteristics of this framework and agents end up behaving independently from each other. In this work we argue that coordination among concurrent agents is crucial for efficient exploration. We introduce coordination in Thompson Sampling based methods by drawing correlated samples from an agent’s posterior. We apply this idea to extend existing exploration schemes such as randomized least squares value iteration (RLSVI). Empirical results on simple toy tasks emphasize the merits of our approach and call attention to coordination as a key objective for efficient exploration in concurrent reinforcement learning.

2022-04-24

ICLR.cc/2022/Workshop/GMS (accepté)

openreview.net

Online Adversarial Attacks

Avishek Joey Bose

William L. Hamilton

Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arri… (voir plus)ves as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial knowledge of the target model, and the decisions made by the attacker are irrevocable since they operate on a transient data stream. We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied

2022-04-24

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Policy Gradients Incorporating the Future

David Venuto

Elaine Lau

Doina Precup

Ofir Nachum

Reasoning about the future -- understanding how decisions in the present time affect outcomes in the future -- is one of the central challen… (voir plus)ges for reinforcement learning (RL), especially in highly-stochastic or partially observable environments. While predicting the future directly is hard, in this work we introduce a method that allows an agent to "look into the future" without explicitly predicting it. Namely, we propose to allow an agent, during its training on past experience, to observe what \emph{actually} happened in the future at that time, while enforcing an information bottleneck to avoid the agent overly relying on this privileged information. This gives our agent the opportunity to utilize rich and useful information about the future trajectory dynamics in addition to the present. Our method, Policy Gradients Incorporating the Future (PGIF), is easy to implement and versatile, being applicable to virtually any policy gradient algorithm. We apply our proposed method to a number of off-the-shelf RL algorithms and show that PGIF is able to achieve higher reward faster in a variety of online and offline RL domains, as well as sparse-reward and partially observable environments.

2022-04-24

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Pre-training Molecular Graph Representation with 3D Geometry

Shengchao Liu

Hanchen Wang

Weiyang Liu

Joan Lasenby

Hongyu Guo

Jian Tang

Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically model… (voir plus)ed by their 2D topological structures, but it has been recently discovered that 3D geometric information plays a more vital role in predicting molecular functionalities. However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation. To cope with this challenge, we propose the Graph Multi-View Pre-training (GraphMVP) framework where self-supervised learning (SSL) is performed by leveraging the correspondence and consistency between 2D topological structures and 3D geometric views. GraphMVP effectively learns a 2D molecular graph encoder that is enhanced by richer and more discriminative 3D geometry. We further provide theoretical insights to justify the effectiveness of GraphMVP. Finally, comprehensive experiments show that GraphMVP can consistently outperform existing graph SSL methods. Code is available on GitHub: https://github.com/chao1224/GraphMVP.

2022-04-24

International Conference on Learning Representations (Accept (Poster))

openreview.net

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