Publications

Deep Reinforcement Learning in Human Activity Recognition: A Survey and Outlook.

Bahareh Nikpour

Dimitrios Sinodinos

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

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

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

Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space

Leo Schwinn

David Dobre

Sophie Xhonneux

Gauthier Gidel

Stephan Günnemann

Current research in adversarial robustness of LLMs focuses on discrete input manipulations in the natural language space, which can be direc… (voir plus)tly transferred to closed-source models. However, this approach neglects the steady progression of open-source models. As open-source models advance in capability, ensuring their safety also becomes increasingly imperative. Yet, attacks tailored to open-source LLMs that exploit full model access remain largely unexplored. We address this research gap and propose the embedding space attack, which directly attacks the continuous embedding representation of input tokens. We find that embedding space attacks circumvent model alignments and trigger harmful behaviors more efficiently than discrete attacks or model fine-tuning. Furthermore, we present a novel threat model in the context of unlearning and show that embedding space attacks can extract supposedly deleted information from unlearned LLMs across multiple datasets and models. Our findings highlight embedding space attacks as an important threat model in open-source LLMs. Trigger Warning: the appendix contains LLM-generated text with violence and harassment.

2024-02-14

ArXiv (prépublication)

doi.org

arxiv.org

Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code

Vahid Majdinasab

Amin Nikanjam

Foutse Khomh

Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not cont… (voir plus)ain code from protected sources. The recent advent of Large Language Models (LLMs) as coding assistants in the software development process poses new challenges for code auditing. The dataset for training these models is mainly collected from publicly available sources. This raises the issue of intellectual property infringement as developers' codes are already included in the dataset. Therefore, auditing code developed using LLMs is challenging, as it is difficult to reliably assert if an LLM used during development has been trained on specific copyrighted codes, given that we do not have access to the training datasets of these models. Given the non-disclosure of the training datasets, traditional approaches such as code clone detection are insufficient for asserting copyright infringement. To address this challenge, we propose a new approach, TraWiC; a model-agnostic and interpretable method based on membership inference for detecting code inclusion in an LLM's training dataset. We extract syntactic and semantic identifiers unique to each program to train a classifier for detecting code inclusion. In our experiments, we observe that TraWiC is capable of detecting 83.87% of codes that were used to train an LLM. In comparison, the prevalent clone detection tool NiCad is only capable of detecting 47.64%. In addition to its remarkable performance, TraWiC has low resource overhead in contrast to pair-wise clone detection that is conducted during the auditing process of tools like CodeWhisperer reference tracker, across thousands of code snippets.

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

Computing Power and the Governance of Artificial Intelligence

Girish Sastry

Lennart Heim

Haydn Belfield

Markus Anderljung

Miles Brundage

Julian Hazell

Cullen C. O'keefe

Gillian K. Hadfield

Richard Ngo

Konstantin Pilz

George Gor

Emma Bluemke

Sarah Shoker

Janet Egan

Robert F. Trager

Shahar Avin

Adrian Weller

Yoshua Bengio

Diane Coyle

Computing power, or"compute,"is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, govern… (voir plus)ments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance.

2024-02-13

ArXiv (prépublication)

doi.org

arxiv.org

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Nicolaus Foerster

Gintare Karolina Dziugaite