Publications

Evaluating Representation Learning on the Protein Structure Universe
Arian R. Jamasb
Alex Morehead
Chaitanya K. Joshi
Zuobai Zhang
Kieran Didi
Simon Mathis
Charles Harris
Jian Tang
Jianlin Cheng
Pietro Lio
Tom L. Blundell
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural … (see more)Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structures to enable the systematic evaluation of the quality of the learned structural representation and their usefulness in capturing functional relationships for downstream tasks. We find that: (1) large-scale pretraining on AlphaFold structures and auxiliary tasks consistently improve the performance of both rotation-invariant and equivariant GNNs, and (2) more expressive equivariant GNNs benefit from pretraining to a greater extent compared to invariant models. We aim to establish a common ground for the machine learning and computational biology communities to rigorously compare and advance protein structure representation learning. Our open-source codebase reduces the barrier to entry for working with large protein structure datasets by providing: (1) storage-efficient dataloaders for large-scale structural databases including AlphaFoldDB and ESM Atlas, as well as (2) utilities for constructing new tasks from the entire PDB. ProteinWorkshop is available at: github.com/a-r-j/ProteinWorkshop.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Expected Flow Networks in Stochastic Environments and Two-Player Zero-Sum Games
Marco Jiralerspong
Bilun Sun
Danilo Vucetic
Tianyu Zhang
Yoshua Bengio
Gauthier Gidel
Nikolay Malkin
Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully … (see more)applied to various structured object generation tasks, sampling a diverse set of high-reward objects quickly. We propose expected flow networks (EFlowNets), which extend GFlowNets to stochastic environments. We show that EFlowNets outperform other GFlowNet formulations in stochastic tasks such as protein design. We then extend the concept of EFlowNets to adversarial environments, proposing adversarial flow networks (AFlowNets) for two-player zero-sum games. We show that AFlowNets learn to find above 80% of optimal moves in Connect-4 via self-play and outperform AlphaZero in tournaments.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Ghost on the Shell: An Expressive Representation of General 3D Shapes
Zhen Liu
Yao Feng
Yuliang Xiu
Weiyang Liu
Liam Paull
Michael J. Black
Bernhard Schölkopf
The creation of photorealistic virtual worlds requires the accurate modeling of 3D surface geometry for a wide range of objects. For this, m… (see more)eshes are appealing since they 1) enable fast physics-based rendering with realistic material and lighting, 2) support physical simulation, and 3) are memory-efficient for modern graphics pipelines. Recent work on reconstructing and statistically modeling 3D shape, however, has critiqued meshes as being topologically inflexible. To capture a wide range of object shapes, any 3D representation must be able to model solid, watertight, shapes as well as thin, open, surfaces. Recent work has focused on the former, and methods for reconstructing open surfaces do not support fast reconstruction with material and lighting or unconditional generative modelling. Inspired by the observation that open surfaces can be seen as islands floating on watertight surfaces, we parameterize open surfaces by defining a manifold signed distance field on watertight templates. With this parameterization, we further develop a grid-based and differentiable representation that parameterizes both watertight and non-watertight meshes of arbitrary topology. Our new representation, called Ghost-on-the-Shell (G-Shell), enables two important applications: differentiable rasterization-based reconstruction from multiview images and generative modelling of non-watertight meshes. We empirically demonstrate that G-Shell achieves state-of-the-art performance on non-watertight mesh reconstruction and generation tasks, while also performing effectively for watertight meshes.
2024-01-15
ICLR.cc/2024/Conference (oral)
doi.org
openreview.net
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
Shengyao Lu
Keith G Mills
Jiao He
Bang Liu
Di Niu
Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for ex… (see more)plaining GNNs typically rely on training auxiliary models, resulting in the explanations remain black-boxed. This paper introduces Graph Output Attribution (GOAt), a novel method to attribute graph outputs to input graph features, creating GNN explanations that are faithful, discriminative, as well as stable across similar samples. By expanding the GNN as a sum of scalar products involving node features, edge features and activation patterns, we propose an efficient analytical method to compute contribution of each node or edge feature to each scalar product and aggregate the contributions from all scalar products in the expansion form to derive the importance of each node and edge. Through extensive experiments on synthetic and real-world data, we show that our method not only outperforms various state-of-the-art GNN explainers in terms of the commonly used fidelity metric, but also exhibits stronger discriminability, and stability by a remarkable margin.
2024-01-15
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Hallucination Detection and Hallucination Mitigation: An Investigation
Junliang Luo
Tianyu Li
Di Wu
M. Jenkin
Steve Liu
Gregory Dudek
2024-01-15
ArXiv (preprint)
doi.org
arxiv.org
How Connectivity Structure Shapes Rich and Lazy Learning in Neural Circuits
Yuhan Helena Liu
Aristide Baratin
Jonathan Cornford
Stefan Mihalas
Eric Shea-Brown
Guillaume Lajoie
In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learn… (see more)ing dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity could exhibit a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights -- in particular their effective rank -- influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Improving Intrinsic Exploration by Creating Stationary Objectives
Roger Creus Castanyer
Joshua Romoff
Glen Berseth
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Intelligent Switching in Reset-Free RL
Darshan Patil
Janarthanan Rajendran
Glen Berseth
Sarath Chandar
In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resett… (see more)ing} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Jointly-Learned Exit and Inference for a Dynamic Neural Network
Florence Regol
Joud Chataoui
Mark J. Coates
2024-01-15
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Large Language Models as Generalizable Policies for Embodied Tasks
Andrew Szot
Max Schwarzer
Harsh Agrawal
Bogdan Mazoure
Walter Talbott
Rin Metcalf
Natalie Mackraz
R Devon Hjelm
Alexander T Toshev
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
Tianhong Li
Sangnie Bhardwaj
Yonglong Tian
Han Zhang
Jarred Barber
Dina Katabi
Guillaume Lajoie
Huiwen Chang
Dilip Krishnan
Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalizati… (see more)on capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more accurate but requires significant manual effort and expense. We introduce
2024-01-15
ICLR.cc/2024/Conference (spotlight)
doi.org
openreview.net
LOQA: Learning with Opponent Q-Learning Awareness
Milad Aghajohari
Juan Agustin Duque
Tim Cooijmans
Aaron Courville
In various real-world scenarios, interactions among agents often resemble the dynamics of general-sum games, where each agent strives to opt… (see more)imize its own utility. Despite the ubiquitous relevance of such settings, decentralized machine learning algorithms have struggled to find equilibria that maximize individual utility while preserving social welfare. In this paper we introduce Learning with Opponent Q-Learning Awareness (LOQA), a novel, decentralized reinforcement learning algorithm tailored to optimizing an agent's individual utility while fostering cooperation among adversaries in partially competitive environments. LOQA assumes the opponent samples actions proportionally to their action-value function Q. Experimental results demonstrate the effectiveness of LOQA at achieving state-of-the-art performance in benchmark scenarios such as the Iterated Prisoner's Dilemma and the Coin Game. LOQA achieves these outcomes with a significantly reduced computational footprint, making it a promising approach for practical multi-agent applications.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net