Publications

MiRGraph: A hybrid deep learning approach to identify microRNA-target interactions by integrating heterogeneous regulatory network and genomic sequences
Pei Liu
Yang Liu
Jiawei Luo
Yuemei Li
2024-10-01
bioRxiv (prépublication)
doi.org
VinePPO: Refining Credit Assignment in RL Training of LLMs
Amirhossein Kazemnejad
Milad Aghajohari
Eva Portelance
Alessandro Sordoni
Siva Reddy
Aaron Courville
Nicolas Le Roux
Large language models (LLMs) are increasingly applied to complex reasoning tasks that require executing several complex steps before receivi… (voir plus)ng any reward. Properly assigning credit to these steps is essential for enhancing model performance. Proximal Policy Optimization (PPO), a common reinforcement learning (RL) algorithm used for LLM finetuning, employs value networks to tackle credit assignment. However, recent approaches achieve strong results without it, raising questions about the efficacy of value networks in practice. In this work, we systematically evaluate the efficacy of value networks and reveal their significant shortcomings in reasoning-heavy LLM tasks, showing that they often produce poor estimate of expected return and barely outperform a random baseline when comparing alternative steps. This motivates our key question: Can improved credit assignment enhance RL training for LLMs? To address this, we propose VinePPO, a straightforward approach that leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates. Our method consistently outperforms PPO and other baselines across MATH and GSM8K datasets in less wall-clock time (up to 3.0x). Crucially, it achieves higher test accuracy for a given training accuracy, capturing more generalization signal per sample. These results emphasize the importance of accurate credit assignment in RL training of LLM.
2024-10-01
arXiv (prépublication)
doi.org
proceedings.mlr.press
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers h… (voir plus)ave since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence length - have sparked renewed interest in novel recurrent models that are parallelizable during training, offer comparable performance, and scale more effectively. In this work, we revisit sequence modelling from a historical perspective, focusing on Recurrent Neural Networks (RNNs), which dominated the field for two decades before the rise of Transformers. Specifically, we examine LSTMs (1997) and GRUs (2014). We demonstrate that by simplifying these models, we can derive minimal versions (minLSTMs and minGRUs) that (1) use fewer parameters than their traditional counterparts, (2) are fully parallelizable during training, and (3) achieve surprisingly competitive performance on a range of tasks, rivalling recent models including Transformers.
2024-10-01
ArXiv (prépublication)
doi.org
arxiv.org
Were RNNs All We Needed?
Leo Feng
Frederick Tung
Mohamed Osama Ahmed
Yoshua Bengio
Hossein Hajimirsadeghi
2024-10-01
ArXiv (prépublication)
doi.org
arxiv.org
Challenges for impact evaluation of WHO’s normative output
Catherine Régis
Gaëlle Foucault
Jean-Louis Denis
Pierre Larouche
Miriam Cohen
2024-09-30
Bulletin of the World Health Organization (publié)
doi.org
Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process
Heng Xia
Jian Tang
Wen Yu
JunFei Qiao
2024-09-30
IEEE transactions on industrial electronics (1982. Print) (publié)
doi.org
Inferring electric vehicle charging patterns from smart meter data for impact studies
Feng Li
Élodie Campeau
Ilhan Kocar
Antoine Lesage-Landry
2024-09-30
Electric power systems research (publié)
doi.org
Long-term outcomes of critically ill patients with hematological malignancies: what is the impact of the coronavirus disease 2019 pandemic? Author's reply
Laveena Munshi
Guillaume Dumas
Sangeeta Mehta
2024-09-30
Intensive Care Medicine (publié)
doi.org
MAP: Model Merging with Amortized Pareto Front Using Limited Computation
Li Li
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
Yoshua Bengio
2024-09-30
NeurIPS.cc/2024/Workshop/Federated_Learning (présentation orale)
openreview.net
openreview.net
Single-Shot Learning of Stable Dynamical Systems for Long-Horizon Manipulation Tasks
Alexandre St-Aubin
Amin Abyaneh
Hsiu-Chin Lin
Mastering complex sequential tasks continues to pose a significant challenge in robotics. While there has been progress in learning long-hor… (voir plus)izon manipulation tasks, most existing approaches lack rigorous mathematical guarantees for ensuring reliable and successful execution. In this paper, we extend previous work on learning long-horizon tasks and stable policies, focusing on improving task success rates while reducing the amount of training data needed. Our approach introduces a novel method that (1) segments long-horizon demonstrations into discrete steps defined by waypoints and subgoals, and (2) learns globally stable dynamical system policies to guide the robot to each subgoal, even in the face of sensory noise and random disturbances. We validate our approach through both simulation and real-world experiments, demonstrating effective transfer from simulation to physical robotic platforms. Code is available at https://github.com/Alestaubin/stable-imitation-policy-with-waypoints
2024-09-30
ArXiv (prépublication)
doi.org
arxiv.org
Spatial Action Unit Cues for Interpretable Deep Facial Expression Recognition
Soufiane Belharbi
Marco Pedersoli
Alessandro Lameiras Koerich
Simon Bacon
Eric Granger
Although state-of-the-art classifiers for facial expression recognition (FER) can achieve a high level of accuracy, they lack interpretabili… (voir plus)ty, an important feature for end-users. Experts typically associate spatial action units (AUs) from a codebook to facial regions for the visual interpretation of expressions. In this paper, the same expert steps are followed. A new learning strategy is proposed to explicitly incorporate AU cues into classifier training, allowing to train deep interpretable models. During training, this AU codebook is used, along with the input image expression label, and facial landmarks, to construct a AU heatmap that indicates the most discriminative image regions of interest w.r.t the facial expression. This valuable spatial cue is leveraged to train a deep interpretable classifier for FER. This is achieved by constraining the spatial layer features of a classifier to be correlated with AU heatmaps. Using a composite loss, the classifier is trained to correctly classify an image while yielding interpretable visual layer-wise attention correlated with AU maps, simulating the expert decision process. Our strategy only relies on image class expression for supervision, without additional manual annotations. Our new strategy is generic, and can be applied to any deep CNN- or transformer-based classifier without requiring any architectural change or significant additional training time. Our extensive evaluation on two public benchmarks RAF-DB, and AffectNet datasets shows that our proposed strategy can improve layer-wise interpretability without degrading classification performance. In addition, we explore a common type of interpretable classifiers that rely on class activation mapping (CAM) methods, and show that our approach can also improve CAM interpretability.
2024-09-30
ArXiv (prépublication)
doi.org
arxiv.org
A Survey of Diversification Techniques in Search and Recommendation
Haolun Wu
Yansen Zhang
Chen Ma
Fuyuan Lyu
Bowei He
Fernando Diaz
Bhaskar Mitra
Xue Liu
Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (voir plus)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey’s main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the openness research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.
2024-09-30
IEEE Transactions on Knowledge and Data Engineering (publié)
doi.org
arxiv.org