Publications

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
Lucas Lehnert
Sainbayar Sukhbaatar
Paul McVay
Michael Rabbat
Yuandong Tian
While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symboli… (voir plus)c planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard
2024-02-21
ArXiv (prépublication)
doi.org
arxiv.org
Searching for Strong Gravitational Lenses
Cameron Lemon
Frederic Courbin
Anupreeta More
Paul Schechter
Raoul Cañameras
Ludovic Delchambre
Calvin Leung
Yiping Shu
Chiara Spiniello
Yashar Hezaveh
Jonas Klüter
Richard G. McMahon
2024-02-21
Space Science Reviews (publié)
doi.org
arxiv.org
Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
Sitao Luan
Mingde Zhao
Xiao-Wen Chang
Doina Precup
2024-02-20
Complex Networks & Their Applications XII (publié)
doi.org
arxiv.org
When Do We Need Graph Neural Networks for Node Classification?
Sitao Luan
Chenqing Hua
Qincheng Lu
Jiaqi Zhu
Xiao-Wen Chang
Doina Precup
2024-02-20
Complex Networks & Their Applications XII (publié)
doi.org
arxiv.org
Deep Reinforcement Learning in Human Activity Recognition: A Survey and Outlook.
Bahareh Nikpour
Dimitrios Sinodinos
Narges Armanfard
Human activity recognition (HAR) is a popular research field in computer vision that has already been widely studied. However, it is still a… (voir plus)n active research field since it plays an important role in many current and emerging real-world intelligent systems, like visual surveillance and human-computer interaction. Deep reinforcement learning (DRL) has recently been used to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based HAR has only been around for a short time, and it is a challenging, novel field of study. Therefore, to facilitate further research in this area, we have constructed a comprehensive survey on activity recognition methods that incorporate DRL. Throughout the article, we classify these methods according to their shared objectives and delve into how they are ingeniously framed within the DRL framework. As we navigate through the survey, we conclude by shedding light on the prominent challenges and lingering questions that await the attention of future researchers, paving the way for further advancements and breakthroughs in this exciting domain.
2024-02-19
IEEE Transactions on Neural Networks and Learning Systems (publié)
doi.org
Multi-ancestry polygenic risk scores using phylogenetic regularization
Elliot Layne
Shadi Zabad
Yue Li
Mathieu Blanchette
2024-02-17
bioRxiv (prépublication)
doi.org
Deep Equilibrium Models For Algorithmic Reasoning
Sophie Xhonneux
Yu He
Andreea Deac
Jian Tang
Gauthier Gidel
In this blogpost we discuss the idea of teaching neural networks to reach fixed points when reasoning. Specifically, on the algorithmic reas… (voir plus)oning benchmark CLRS the current neural networks are told the number of reasoning steps they need. While a quick fix is to add a termination network that predicts when to stop, a much more salient inductive bias is that the neural network shouldn't change it's answer any further once the answer is correct, i.e. it should reach a fixed point. This is supported by denotational semantics, which tells us that while loops that terminate are the minimum fixed points of a function. We implement this idea with the help of deep equilibrium models and discuss several hurdles one encounters along the way. We show on several algorithms from the CLRS benchmark the partial success of this approach and the difficulty in making it work robustly across all algorithms.
2024-02-16
ICLR.cc/2024/BlogPosts (publié)
openreview.net
openreview.net
Deep Equilibrium Models For Algorithmic Reasoning
Sophie Xhonneux
Yu He
Andreea Deac
Jian Tang
Gauthier Gidel
In this blogpost we discuss the idea of teaching neural networks to reach fixed points when reasoning. Specifically, on the algorithmic reas… (voir plus)oning benchmark CLRS the current neural networks are told the number of reasoning steps they need. While a quick fix is to add a termination network that predicts when to stop, a much more salient inductive bias is that the neural network shouldn't change it's answer any further once the answer is correct, i.e. it should reach a fixed point. This is supported by denotational semantics, which tells us that while loops that terminate are the minimum fixed points of a function. We implement this idea with the help of deep equilibrium models and discuss several hurdles one encounters along the way. We show on several algorithms from the CLRS benchmark the partial success of this approach and the difficulty in making it work robustly across all algorithms.
2024-02-16
ICLR.cc/2024/BlogPosts (accepté)
openreview.net
openreview.net
Distributional GFlowNets with Quantile Flows
Dinghuai Zhang
Ling Pan
Ricky T. Q. Chen
Aaron Courville
Yoshua Bengio
Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating com… (voir plus)plex combinatorial structure through a series of decision-making steps. Despite being inspired from reinforcement learning, the current GFlowNet framework is relatively limited in its applicability and cannot handle stochasticity in the reward function. In this work, we adopt a distributional paradigm for GFlowNets, turning each flow function into a distribution, thus providing more informative learning signals during training. By parameterizing each edge flow through their quantile functions, our proposed \textit{quantile matching} GFlowNet learning algorithm is able to learn a risk-sensitive policy, an essential component for handling scenarios with risk uncertainty. Moreover, we find that the distributional approach can achieve substantial improvement on existing benchmarks compared to prior methods due to our enhanced training algorithm, even in settings with deterministic rewards.
2024-02-16
TMLR (accepté)
doi.org
openreview.net
Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats
Léo Choinière
Rose Guay-Hottin
Rémi Picard
Guillaume Lajoie
Marco Bonizzato
Numa Dancause
2024-02-14
STAR Protocols (publié)
doi.org
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy
Brice Rauby
Paul Xing
Jonathan Por'ee
Maxime Gasse
Jean Provost
2024-02-14
ArXiv (prépublication)
doi.org
arxiv.org
ChatGPT vs LLaMA: Impact, Reliability, and Challenges in Stack Overflow Discussions
Léuson M. P. Da Silva
Jordan Samhi
Foutse Khomh
Since its release in November 2022, ChatGPT has shaken up Stack Overflow, the premier platform for developers' queries on programming and so… (voir plus)ftware development. Demonstrating an ability to generate instant, human-like responses to technical questions, ChatGPT has ignited debates within the developer community about the evolving role of human-driven platforms in the age of generative AI. Two months after ChatGPT's release, Meta released its answer with its own Large Language Model (LLM) called LLaMA: the race was on. We conducted an empirical study analyzing questions from Stack Overflow and using these LLMs to address them. This way, we aim to (ii) measure user engagement evolution with Stack Overflow over time; (ii) quantify the reliability of LLMs' answers and their potential to replace Stack Overflow in the long term; (iii) identify and understand why LLMs fails; and (iv) compare LLMs together. Our empirical results are unequivocal: ChatGPT and LLaMA challenge human expertise, yet do not outperform it for some domains, while a significant decline in user posting activity has been observed. Furthermore, we also discuss the impact of our findings regarding the usage and development of new LLMs.
2024-02-13
ArXiv (prépublication)
doi.org
arxiv.org