Yoshua Bengio

Scaling in the Service of Reasoning & Model-Based ML

April 4, 2023

Yoshua Bengio

Edward J. Hu

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

March 23, 2022

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

March 15, 2022

Generative Flow Networks

Yoshua Bengio

Publications

GFlowNets for Causal Discovery: an Overview

Dragos Cristian Manta

Edward J Hu

2023-06-18

ICML.cc/2023/Workshop/SPIGM (poster)

Thompson Sampling for Improved Exploration in GFlowNets

Jarrid Rector-Brooks

Kanika Madan

Moksh J. Jain

Maksym Korablyov

Cheng-Hao Liu

A. Chandar

Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over composition… (see more)al objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for hierarchical sampling that optimize a variational bound, GFlowNet algorithms can stably run off-policy, which can be advantageous for discovering modes of the target distribution. Despite this flexibility in the choice of behaviour policy, the optimal way of efficiently selecting trajectories for training has not yet been systematically explored. In this paper, we view the choice of trajectories for training as an active learning problem and approach it using Bayesian techniques inspired by methods for multi-armed bandits. The proposed algorithm, Thompson sampling GFlowNets (TS-GFN), maintains an approximate posterior distribution over policies and samples trajectories from this posterior for training. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.

2023-06-18

ICML.cc/2023/Workshop/SPIGM (poster)

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior

Ayush K Chakravarthy

Trang M. Nguyen

Anirudh Goyal

Michael Curtis Mozer

2023-05-30

ArXiv (preprint)

arxiv.org

Model evaluation for extreme risks

Toby Shevlane

Sebastian Farquhar

Ben Garfinkel

Mary Phuong

Jess Whittlestone

Jade Leung

Daniel Kokotajlo

Nahema A. Marchal

Markus Anderljung

Noam Kolt

Lewis Ho

Divya Siddarth

Shahar Avin

W. Hawkins

Been Kim

Iason Gabriel

Vijay Bolina

Jack Clark

Paul F. Christiano … (see 1 more)

Allan Dafoe

2023-05-23

ArXiv (preprint)

arxiv.org

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days

Colleen J. Gillon

Jérôme A. Lecoq

Jason E. Pina

Ruweida Ahmed

Yazan N. Billeh

Shiella Caldejon

Peter Groblewski

Timothy M. Henley

India Kato

Eric Lee

Jennifer Luviano

Kyla Mace

Chelsea Nayan

Thuyanh V. Nguyen

Kat North

Jed Perkins

Sam Seid

Matthew T. Valley

Ali Williford

Yoshua Bengio … (see 3 more)

Timothy P. Lillicrap

Joel Zylberberg

Blake A. Richards

The apical dendrites of pyramidal neurons in sensory cortex receive primarily top-down signals from associative and motor regions, while cel… (see more)l bodies and nearby dendrites are heavily targeted by locally recurrent or bottom-up inputs from the sensory periphery. Based on these differences, a number of theories in computational neuroscience postulate a unique role for apical dendrites in learning. However, due to technical challenges in data collection, little data is available for comparing the responses of apical dendrites to cell bodies over multiple days. Here we present a dataset collected through the Allen Institute Mindscope’s OpenScope program that addresses this need. This dataset comprises high-quality two-photon calcium imaging from the apical dendrites and the cell bodies of visual cortical pyramidal neurons, acquired over multiple days in awake, behaving mice that were presented with visual stimuli. Many of the cell bodies and dendrite segments were tracked over days, enabling analyses of how their responses change over time. This dataset allows neuroscientists to explore the differences between apical and somatic processing and plasticity.

2023-05-16

Scientific Data (published)

Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model

Mahsa Taghiakbari

Sina Hamidi Ghalehjegh

Emmanuel Jehanno

Tess Berthier

Lisa Di Jorio

Saber Ghadakzadeh

Alan Barkun

Mark Takla

Mickael Bouin

Eric Deslandres

Simon Bouchard

Sacha Sidani

Daniel von Renteln

Abstract Background and aims Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm comple… (see more)teness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps. Methods We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three nonoverlapping training, validation, and test data sets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). A DCNN classification model was developed, validated, and tested in separate data sets of images containing the four different landmarks. Results After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.7%). The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.4% (95% CI 84.1% to 88.5%), and 86.4% (95% CI 84.1% to 88.6%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.6% (95% CI 86.6% to 90.3%). Conclusion This model offers a novel tool to assist endoscopists with automated identification of AO and ICV during colonoscopy. The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality.

2023-05-01

Journal of the Canadian Association of Gastroenterology (published)

Combining Parameter-efficient Modules for Task-level Generalisation

Edoardo M. Ponti

Alessandro Sordoni

Siva Reddy

A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to ne… (see more)w tasks. In this work, we assume that each task is associated with a subset of latent skills from an (arbitrary size) inventory. In turn, each skill corresponds to a parameter-efficient (sparse / low-rank) model adapter. By jointly learning adapters and a routing function that allocates skills to each task, the full network is instantiated as the average of the parameters of active skills. We propose several inductive biases that encourage re-usage and composition of the skills, including variable-size skill allocation and a dual-speed learning rate. We evaluate our latent-skill model in two main settings: 1) multitask reinforcement learning for instruction following on 8 levels of the BabyAI platform; and 2) few-shot fine-tuning of language models on 160 NLP tasks of the CrossFit benchmark. We find that the modular design of our network enhances sample efficiency in reinforcement learning and few-shot generalisation in supervised learning, compared to a series of baselines. These include models where parameters are fully shared, task-specific, conditionally generated (HyperFormer), or sparse mixture-of-experts (TaskMoE).

2023-04-30

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (published)

Hyena Hierarchy: Towards Larger Convolutional Language Models

Michael Poli

Stefano Massaroli

Eric Nguyen

Daniel Y Fu

Tri Dao

Stephen Baccus

Stefano Ermon

Christopher Re

2023-04-23

ICML.cc/2023/Conference (poster)

proceedings.mlr.press

Interventional Causal Representation Learning

Kartik Ahuja

Yixin Wang

Divyat Mahajan

Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observa… (see more)tional data and structural assumptions (e.g., conditional independence) to identify the latent factors. However, interventional data is prevalent across applications. Can interventional data facilitate causal representation learning? We explore this question in this paper. The key observation is that interventional data often carries geometric signatures of the latent factors' support (i.e. what values each latent can possibly take). For example, when the latent factors are causally connected, interventions can break the dependency between the intervened latents' support and their ancestors'. Leveraging this fact, we prove that the latent causal factors can be identified up to permutation and scaling given data from perfect

2023-04-23

ICML.cc/2023/Conference (poster)

proceedings.mlr.press

Proactive Contact Tracing

Prateek Gupta

Tegan Maharaj

Martin Weiss

Nasim Rahaman

Hannah Alsdurf

Nanor Minoyan

Soren Harnois-Leblanc

Joanna Merckx

Andrew Williams

Victor Schmidt

Pierre-Luc St-Charles

Akshay Patel

Yang Zhang

David L. Buckeridge

Christopher Pal

Bernhard Schölkopf

The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting d… (see more)aily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.

2023-02-28

PLOS Digital Health (published)

DEUP: Direct Epistemic Uncertainty Prediction

Moksh J. Jain

Victor I Butoi

Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence. While existing work focuses on… (see more) using the variance of the Bayesian posterior due to parameter uncertainty as a measure of epistemic uncertainty, we argue that this does not capture the part of lack of knowledge induced by model misspecification. We discuss how the excess risk, which is the gap between the generalization error of a predictor and the Bayes predictor, is a sound measure of epistemic uncertainty which captures the effect of model misspecification. We thus propose a principled framework for directly estimating the excess risk by learning a secondary predictor for the generalization error and subtracting an estimate of aleatoric uncertainty, i.e., intrinsic unpredictability. We discuss the merits of this novel measure of epistemic uncertainty, and highlight how it differs from variance-based measures of epistemic uncertainty and addresses its major pitfall. Our framework, Direct Epistemic Uncertainty Prediction (DEUP) is particularly interesting in interactive learning environments, where the learner is allowed to acquire novel examples in each round. Through a wide set of experiments, we illustrate how existing methods in sequential model optimization can be improved with epistemic uncertainty estimates from DEUP, and how DEUP can be used to drive exploration in reinforcement learning. We also evaluate the quality of uncertainty estimates from DEUP for probabilistic image classification and predicting synergies of drug combinations.

2023-02-12

TMLR (accepted)

Generative Augmented Flow Networks

Longbo Huang

The Generative Flow Network is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the prob… (see more)ability of generating an object is proportional to a given reward function. Its effectiveness has been shown in discovering high-quality and diverse solutions, compared to reward-maximizing reinforcement learning-based methods. Nonetheless, GFlowNets only learn from rewards of the terminal states, which can limit its applicability. Indeed, intermediate rewards play a critical role in learning, for example from intrinsic motivation to provide intermediate feedback even in particularly challenging sparse reward tasks. Inspired by this, we propose Generative Augmented Flow Networks (GAFlowNets), a novel learning framework to incorporate intermediate rewards into GFlowNets. We specify intermediate rewards by intrinsic motivation to tackle the exploration problem in sparse reward environments. GAFlowNets can leverage edge-based and state-based intrinsic rewards in a joint way to improve exploration. Based on extensive experiments on the GridWorld task, we demonstrate the effectiveness and efficiency of GAFlowNet in terms of convergence, performance, and diversity of solutions. We further show that GAFlowNet is scalable to a more complex and large-scale molecule generation domain, where it achieves consistent and significant performance improvement.

2023-01-31

ICLR.cc/2023/Conference (notable)