Publications

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold

Lazar Atanackovic

Xi Zhang

Brandon Amos

Mathieu Blanchette

Leo J Lee

Yoshua Bengio

Alexander Tong

Kirill Neklyudov

Numerous biological and physical processes can be modeled as systems of interacting samples evolving continuously over time, e.g. the dynami… (voir plus)cs of communicating cells or physical particles. Flow-based models allow for learning these dynamics at the population level --- they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We propose

2024-06-17

ICML.cc/2024/Workshop/GRaM (publié)

openreview.net

Neural Ratio Estimators Meet Distributional Shift and Mode Misspecification: A Cautionary Tale from Strong Gravitational Lensing

Andreas Filipp

Yashar Hezaveh

Laurence Perreault-Levasseur

In recent years, there has been increasing interest in the field of astrophysics in applying Neural Ratio Estimators (NREs) to large-scale i… (voir plus)nference problems where both amortization and marginalization over a large number of nuisance parameters are needed. Here, in order to assess the true potential of this method to produce unbiased inference on real data, we investigate the robustness of NREs to distribution shifts and model misspecification in the specific scientific application of the measurement of dark matter population-level parameters using strong gravitational lensing. We investigate the behaviour of a trained NRE for test data presenting distributional shifts inside the bounds of training, as well as out of distribution, both in the linear and non-linear parameters of this problem. While our results show that NREs perform when tested perfectly in distribution, we find that they exhibit significant biases and drawbacks when confronted with slight deviations from the examples seen in the training distribution. This indicates the necessity for caution when applying NREs to real astrophysical data, where underlying distributions are not perfectly known and models do not perfectly reconstruct the true underlying distributions.

2024-06-17

ICML.cc/2024/Workshop/SPIGM (poster)

openreview.net

QGFN: Controllable Greediness with Action Values

Elaine Lau

Stephen Zhewen Lu

Ling Pan

Doina Precup

Emmanuel Bengio

Generative Flow Networks (GFlowNets; GFNs) are a family of reward/energy-based generative methods for combinatorial objects, capable of gene… (voir plus)rating diverse and high-utility samples. However, biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose to combine the GFN policy with an action-value estimate,

2024-06-17

ICML.cc/2024/Workshop/SPIGM (poster)

doi.org

openreview.net

Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models

Eva Portelance

Siva Reddy

Timothy John O'donnell

Semantic and syntactic bootstrapping posit that children use their prior knowledge of one linguistic domain, say syntactic relations, to hel… (voir plus)p later acquire another, such as the meanings of new words. Empirical results supporting both theories may tempt us to believe that these are different learning strategies, where one may precede the other. Here, we argue that they are instead both contingent on a more general learning strategy for language acquisition: joint learning. Using a series of neural visually-grounded grammar induction models, we demonstrate that both syntactic and semantic bootstrapping effects are strongest when syntax and semantics are learnt simultaneously. Joint learning results in better grammar induction, realistic lexical category learning, and better interpretations of novel sentence and verb meanings. Joint learning makes language acquisition easier for learners by mutually constraining the hypotheses spaces for both syntax and semantics. Studying the dynamics of joint inference over many input sources and modalities represents an important new direction for language modeling and learning research in both cognitive sciences and AI, as it may help us explain how language can be acquired in more constrained learning settings.

2024-06-17

ArXiv (prépublication)

doi.org

arxiv.org

RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content

Joao Monteiro

Pierre-Andre Noel

Étienne Marcotte

Sai Rajeswar

Valentina Zantedeschi

David Vazquez

Nicolas Chapados

Chris Pal

Perouz Taslakian

Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includ… (voir plus)es encyclopedic documents that harbor a vast amount of general knowledge (e.g., Wikipedia) but also potentially overlap with benchmark datasets used for evaluating LLMs. Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions. To foster sound evaluation of language models, we introduce a new test dataset named RepLiQA, suited for question-answering and topic retrieval tasks. RepLiQA is a collection of five splits of test sets, four of which have not been released to the internet or exposed to LLM APIs prior to this publication. Each sample in RepLiQA comprises (1) a reference document crafted by a human annotator and depicting an imaginary scenario (e.g., a news article) absent from the internet; (2) a question about the document's topic; (3) a ground-truth answer derived directly from the information in the document; and (4) the paragraph extracted from the reference document containing the answer. As such, accurate answers can only be generated if a model can find relevant content within the provided document. We run a large-scale benchmark comprising several state-of-the-art LLMs to uncover differences in performance across models of various types and sizes in a context-conditional language modeling setting. Released splits of RepLiQA can be found here: https://huggingface.co/datasets/ServiceNow/repliqa.

2024-06-17

ArXiv (prépublication)

doi.org

arxiv.org

Revisiting Successor Features for Inverse Reinforcement Learning

Arnav Kumar Jain

Harley Wiltzer

Jesse Farebrother

Irina Rish

Glen Berseth

Sanjiban Choudhury

2024-06-17

ICML.cc/2024/Workshop/MFHAIA (poster)

openreview.net

RGFN: Synthesizable Molecular Generation Using GFlowNets

Michał Koziarski

Andrei Rekesh

Dmytro Shevchuk

Almer M. van der Sloot

Piotr Gaiński

Yoshua Bengio

Cheng-Hao Liu

Mike Tyers

Robert A. Batey

2024-06-17

ICML.cc/2024/Workshop/ML4LMS (poster)

doi.org

openreview.net

On The Local Geometry of Deep Generative Manifolds

Ahmed Imtiaz Humayun

Ibtihel Amara

Candice Schumann

Golnoosh Farnadi

Negar Rostamzadeh

Mohammad Havaei

In this paper, we study theoretically inspired local geometric descriptors of the data manifolds approximated by pre-trained generative mode… (voir plus)ls. The descriptors – local scaling (ψ), local rank (ν), and local complexity (δ) — characterize the uncertainty, dimensionality, and smoothness on the learned manifold, using only the network weights and architecture. We investigate and emphasize their critical role in understanding generative models. Our analysis reveals that the local geometry is intricately linked to the quality and diversity of generated outputs. Additionally, we see that the geometric properties are distinct for out-of-distribution (OOD) inputs as well as for prompts memorized by Stable Diffusion, showing the possible application of our proposed descriptors for downstream detection and assessment of pre-trained generative models.

2024-06-17

ICML.cc/2024/Workshop/GRaM (publié)

openreview.net

Using neural biomarkers to personalize dosing of vagus nerve stimulation

Antonin Berthon

Lorenz Wernisch

Myrta Stoukidi

Michael Thornton

Olivier Tessier-Lariviere

Pascal Fortier-Poisson

Jorin Mamen

Max Pinkney

Susannah Lee

Elvijs Sarkans

Luca Annecchino

Ben Appleton

Philip Garsed

Bret Patterson

Samuel Gonshaw

Matjaž Jakopec

Sudhakaran Shunmugam

Tristan Edwards

Aleksi Tukiainen

Joel Jennings … (voir 3 de plus)

Guillaume Lajoie

Emil Hewage

Oliver Armitage

2024-06-17

Bioelectronic Medicine (publié)

doi.org

Cell Morphology-Guided Small Molecule Generation with GFlowNets

Stephen Zhewen Lu

Ziqing Lu

Ehsan Hajiramezanali

Tommaso Biancalani

Yoshua Bengio

Gabriele Scalia

Michał Koziarski

2024-06-16

ICML.cc/2024/Workshop/AI4Science (poster)

doi.org

openreview.net

Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding

Le Zhang

Rabiul Awal

Aishwarya Agrawal

Vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream … (voir plus)tasks such as zero-shot image classification, image-text retrieval, and text-to-image generation. However, the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally, the current contrastive learning objective fails to focus on fine-grained grounding components like relations, actions, and attributes, resulting in"bag-of-words"representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP, our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks. We open-source our code at https://github.com/lezhang7/Enhance-FineGrained.

2024-06-16

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

doi.org

arxiv.org

Expressivity of Neural Networks with Fixed Weights and Learned Biases

Ezekiel Williams

Avery Hee-Woon Ryoo

Thomas Jiralerspong

Alexandre Payeur

Matt Perich

Luca Mazzucato

Guillaume Lajoie