Publications

Improving Intrinsic Exploration by Creating Stationary Objectives
Roger Creus Castanyer
Joshua Romoff
Glen Berseth
2023-10-27
ArXiv (prépublication)
doi.org
arxiv.org
Learning Macro Variables with Auto-encoders
Dhanya Sridhar
Eric Elmoznino
Maitreyi Swaroop
2023-10-27
NeurIPS.cc/2023/Workshop/CRL (poster)
openreview.net
openreview.net
Learning Optimizers for Local SGD
Charles-Étienne Joseph
Benjamin Thérien
Abhinav Moudgil
Boris Knyazev
Eugene Belilovsky
2023-10-27
NeurIPS.cc/2023/Workshop/Federated_Learning (poster)
openreview.net
openreview.net
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim
Joohwan Ko
Dinghuai Zhang
Ling Pan
Taeyoung Yun
Woo Chang Kim
Jinkyoo Park
Yoshua Bengio
GFlowNets are probabilistic models that learn a stochastic policy that sequentially generates compositional structures, such as molecular gr… (voir plus)aphs. They are trained with the objective of sampling such objects with probability proportional to the object's reward. Among GFlowNets, the temperature-conditional GFlowNets represent a family of policies indexed by temperature, and each is associated with the correspondingly tempered reward function. The major benefit of temperature-conditional GFlowNets is the controllability of GFlowNets' exploration and exploitation through adjusting temperature. We propose a \textit{Learning to Scale Logits for temperature-conditional GFlowNets} (LSL-GFN), a novel architectural design that greatly accelerates the training of temperature-conditional GFlowNets. It is based on the idea that previously proposed temperature-conditioning approaches introduced numerical challenges in the training of the deep network because different temperatures may give rise to very different gradient profiles and ideal scales of the policy's logits. We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly. We empirically show that our strategy dramatically improves the performances of GFlowNets, outperforming other baselines, including reinforcement learning and sampling methods, in terms of discovering diverse modes in multiple biochemical tasks.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Science (poster)
doi.org
openreview.net
Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer
Jesse Farebrother
Joshua Greaves
Kevin Roccapriore
Ekin Dogus Cubuk
Rishabh Agarwal
Aaron Courville
Marc Gendron-Bellemare
Sergei Kalinin
Igor Mordatch
Pablo Samuel Castro
We introduce a machine learning approach to determine the transition rates of silicon atoms on a single layer of carbon atoms, when stimulat… (voir plus)ed by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition rates. These rates are then applied to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)
openreview.net
openreview.net
Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer
Jesse Farebrother
Joshua Greaves
Kevin Roccapriore
Ekin Dogus Cubuk
Rishabh Agarwal
Aaron Courville
Marc Gendron-Bellemare
Sergei Kalinin
Igor Mordatch
Pablo Samuel Castro
We introduce a machine learning approach to determine the transition rates of silicon atoms on a single layer of carbon atoms, when stimulat… (voir plus)ed by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition rates. These rates are then applied to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)
openreview.net
openreview.net
Role of Structural and Conformational Diversity for Machine Learning Potentials
Nikhil Shenoy
Prudencio Tossou
Emmanuel Noutahi
Hadrien Mary
Dominique Beaini
Jiarui Ding
In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically … (voir plus)conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts. We investigate these dynamics through two distinct experiments: a fixed budget one, where the dataset size remains constant, and a fixed molecular set one, which focuses on fixed structural diversity while varying conformational diversity. Our results reveal nuanced patterns in generalization metrics. Notably, for optimal structural and conformational generalization, a careful balance between structural and conformational diversity is required, but existing QM datasets do not meet that trade-off. Additionally, our results highlight the limitation of the MLIP models at generalizing beyond their training distribution, emphasizing the importance of defining applicability domain during model deployment. These findings provide valuable insights and guidelines for QM data generation efforts.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Science (présentation orale)
doi.org
openreview.net
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Cian Eastwood
Julius von Kügelgen
Linus Ericsson
Diane Bouchacourt
Pascal Vincent
Mark Ibrahim
Bernhard Schölkopf
Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data. However, … (voir plus)with downstream tasks generally unknown at training time, it is difficult to deduce a priori which attributes of the data are indeed "style" and can be safely discarded. To address this, we introduce a more principled approach that seeks to disentangle style features rather than discard them. The key idea is to add multiple style embedding spaces where: (i) each is invariant to all-but-one augmentation; and (ii) joint entropy is maximized. We formalize our structured data-augmentation procedure from a causal latent-variable-model perspective, and prove identifiability of both content and (multiple blocks of) style variables. We empirically demonstrate the benefits our approach on synthetic datasets and then present promising but limited results on ImageNet.
2023-10-27
NeurIPS.cc/2023/Workshop/CRL (poster)
openreview.net
openreview.net
SORBET: A Siamese Network for Ontology Embeddings Using a Distance-Based Regression Loss and BERT
Francis Gosselin
Amal Zouaq
2023-10-27
The Semantic Web – ISWC 2023 (publié)
doi.org
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
Alvaro Carbonero
Alexandre AGM Duval
Victor Schmidt
Santiago Miret
Alex Hernandez-Garcia
Yoshua Bengio
David Rolnick
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorpor… (voir plus)ate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Mat (poster)
doi.org
openreview.net
Towards equilibrium molecular conformation generation with GFlowNets
Alexandra Volokhova
Michał Koziarski
Alex Hernandez-Garcia
Cheng-Hao Liu
Santiago Miret
Pablo Lemos
Luca Thiede
Zichao Yan
Alan Aspuru-Guzik
Yoshua Bengio
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this pa… (voir plus)per we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Mat (poster)
doi.org
openreview.net
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng
Florence Regol
Mark Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time… (voir plus) intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. This allows us to fully leverage the high dimensional modeling capability of modern generative models. Our model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPP.
2023-10-26
ArXiv (prépublication)
doi.org
arxiv.org