307 citations · 1.8k across the 219 of their papers we have counts for
33 papers · 1 filter
Sliding Window Recurrences for Sequence Models
Dragos Secrieru, Garyk Brixi, Yoshua Bengio +3
Multi-hybrid architectures are poised to take over language modeling due to better quality and performance. We introduce a hierarchical decomposition framework for linear recurrenc…
FALCON: Few-step Accurate Likelihoods for Continuous Flows
Danyal Rehman, Tara Akhound-Sadegh, Artem Gazizov +2
Scalable sampling of molecular states in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann Generators tackle this problem by pairing a genera…
Adsorption energies are necessary but not sufficient to identify good catalysts
Shahana Chatterjee, Alexander Davis, Lena Podina +7
As a core technology for green chemical synthesis and electrochemical energy storage, electrocatalysis is central to decarbonization strategies aimed at combating climate change. I…
A Comedy of Estimators: On KL Regularization in RL Training of LLMs
Vedant Shah, Johan Obando-Ceron, Vineet Jain +10
The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL objective for LLM training involve…
Active Attacks: Red-teaming LLMs via Adaptive Environments
Taeyoung Yun, Pierre-Luc St-Charles, Jinkyoo Park +2
We address the challenge of generating diverse attack prompts for large language models (LLMs) that elicit harmful behaviors (e.g., insults, sexual content) and are used for safety…
Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
Lena Podina, Christina Humer, Alexandre Duval +7
Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despite fluctuations in sources such as wind a…
Scaling Latent Reasoning via Looped Language Models
Rui-Jie Zhu, Zixuan Wang, Kai Hua +30
Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training…
Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogates
Ramón Nartallo-Kaluarachchi, Shashanka Ubaru, Małgorzata J Zimoń +4
Sequential generative models conditioned on uncertain rewards are central to AI-driven scientific discovery, yet the epistemic uncertainty they inherit from imperfect reward estima…
Learning What Matters: Steering Diffusion via Spectrally Anisotropic Forward Noise
Luca Scimeca, Thomas Jiralerspong, Berton Earnshaw +2
Diffusion Probabilistic Models (DPMs) have achieved strong generative performance, yet their inductive biases remain largely implicit. In this work, we aim to build inductive biase…
Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, Vineet Jain, Sarthak Mittal +9
Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference to make a prediction. Inference-time co…
Relative Trajectory Balance is equivalent to Trust-PCL
Tristan Deleu, Padideh Nouri, Yoshua Bengio +1
Recent progress in generative modeling has highlighted the importance of Reinforcement Learning (RL) for fine-tuning, with KL-regularized methods in particular proving to be highly…
Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning
Jaesik Yoon, Hyeonseo Cho, Yoshua Bengio +1
Diffusion models have recently emerged as a powerful approach for trajectory planning. However, their inherently non-sequential nature limits their effectiveness in long-horizon re…
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng, Arthur Ouaknine, Etienne Laliberté +3
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring methods involve ground measurements, req…
Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators
Danyal Rehman, Oscar Davis, Jiarui Lu +5
Simulation-free training frameworks have been at the forefront of the generative modelling revolution in continuous spaces, leading to large-scale diffusion and flow matching model…
Structure-Aligned Protein Language Model
Can Chen, David Heurtel-Depeiges, Robert M. Vernon +3
Protein language models (pLMs) pre-trained on vast protein sequence databases excel at various downstream tasks but often lack the structural knowledge essential for some biologica…
Self-Evolving Curriculum for LLM Reasoning
Xiaoyin Chen, Jiarui Lu, Minsu Kim +6
Reinforcement learning (RL) has proven effective for fine-tuning large language models (LLMs), significantly enhancing their reasoning abilities in domains such as mathematics and…
Adaptive Inference-Time Scaling via Cyclic Diffusion Search
Gyubin Lee, Truong Nhat Nguyen Bao, Jaesik Yoon +4
Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. However, most inference-time scaling metho…
Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Minsu Kim, Jean-Pierre Falet, Oliver E. Richardson +5
Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can contain inaccurate statements that reduce pe…
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng, Arthur Ouaknine, Etienne Laliberté +3
The potential of tree planting as a natural climate solution is often undermined by inadequate monitoring of tree planting projects. Current monitoring methods involve measuring tr…
A scalable gene network model of regulatory dynamics in single cells
Paul Bertin, Joseph D. Viviano, Alejandro Tejada-Lapuerta +4
Single-cell data provide high-dimensional measurements of the transcriptional states of cells, but extracting insights into the regulatory functions of genes, particularly identify…
Solving Bayesian inverse problems with diffusion priors and off-policy RL
Luca Scimeca, Siddarth Venkatraman, Moksh Jain +14
This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (RL) objective that can asymptotically sol…
Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
Thomas Jiralerspong, Berton Earnshaw, Jason Hartford +2
Diffusion Probabilistic Models (DPMs) are powerful generative models that have achieved unparalleled success in a number of generative tasks. In this work, we aim to build inductiv…
Learning Decision Trees as Amortized Structure Inference
Mohammed Mahfoud, Ghait Boukachab, Michał Koziarski +4
Building predictive models for tabular data presents fundamental challenges, notably in scaling consistently, i.e., more resources translating to better performance, and generalizi…
Extendable Planning via Multiscale Diffusion
Chang Chen, Hany Hamed, Doojin Baek +4
Long-horizon planning is crucial in complex environments, but diffusion-based planners like Diffuser are limited by the trajectory lengths observed during training. This creates a…