Publications

GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Yu Li
Jun Ding
Yue Li
2024-05-17
Lecture Notes in Computer Science (published)
doi.org
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Liam Hodgson
Yue Li
Yasser Iturria-Medina
Jo Anne Stratton
Guy Wolf
Smita Krishnaswamy
David A. Bennett
Danilo Bzdok
2024-05-17
Communications Biology (published)
doi.org
Data Selection for Transfer Unlearning
Nazanin Mohammadi Sepahvand
Vincent Dumoulin
Eleni Triantafillou
Gintare Karolina Dziugaite
2024-05-16
ArXiv (preprint)
doi.org
arxiv.org
Towards a framework selection for assessing the performance of photovoltaic solar power plants: criteria determination
Meryam Chafiq
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
Mastery of Key Performance Indicators (KPIs) in the realm of photovoltaic solar power plants is pivotal for evaluating their effectiveness a… (see more)nd fine-tuning their operational efficiency. The assessment of these plants' performance has con-sistently stood as a focal point in scientific research. Nevertheless, the investigation into the process of selecting a framework for classifying KPIs, particularly through their categorization based on criteria, sub-criteria, or aspects, has been relatively limited in research. This article addresses this gap by conducting a comprehensive literature review on various KPIs and, drawing upon both literature and practical experience, formulating a set of criteria to serve as the foundation for a Multi-Criteria Decision Analysis (MCDA) method. This intricate taxonomic framework enhances the understanding of infrastructure performance for stakeholders in the solar industry. By streamlining decision-making, it simplifies the selection of KPIs tailored to specific requirements, thus mitigating the complexity arising from the abundance of KPIs in the literature. As a result, decision-makers can make well-informed choices regarding the monitoring and evaluation framework that best suits the performance goals of their solar plant.
2024-05-16
2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (published)
doi.org
LLMs can learn self-restraint through iterative self-reflection
Alexandre Piché
Aristides Milios
Dzmitry Bahdanau
Chris Pal
2024-05-15
ArXiv (preprint)
doi.org
arxiv.org
Unmasking Efficiency: Learning Salient Sparse Models in Non-IID Federated Learning
Riyasat Ohib
Bishal Thapaliya
Gintare Karolina Dziugaite
Jingyu Liu 0001
Vince D. Calhoun
Sergey M. Plis
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient commu… (see more)nication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are communicated each round between the clients and the server. We validate SSFL's effectiveness using standard non-IID benchmarks, noting marked improvements in the sparsity--accuracy trade-offs. Finally, we deploy our method in a real-world federated learning framework and report improvement in communication time.
2024-05-15
ArXiv (preprint)
doi.org
arxiv.org
Preface of UniReps: the First Workshop on Unifying Representations in Neural Models
Marco Fumero
Emanuele Rodolá
Clementine Domine
Francesco Locatello
Karolina Dziugaite
Caron Mathilde
Discover why, when and how distinct learning processes yield similar representations, and the degree to which these can be unified.
2024-05-14
Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models (published)
proceedings.mlr.press
Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique
Manqi Zhou
Hao Zhang
Zilong Bai
Dylan Mann-Krzisnik
Fei Wang
Yue Li
2024-05-14
STAR Protocols (published)
doi.org
What Mechanisms Does Knowledge Distillation Distill?
Cindy Wu
Ekdeep Singh Lubana
Bruno Mlodozeniec
Robert Kirk
David Scott Krueger
Knowledge distillation is a commonly-used compression method in ML due to the popularity of increasingly large-scale models, but it is uncle… (see more)ar if all the information a teacher model contains is distilled into the smaller student model. We aim to formalize the concept of ‘knowledge’ to investigate how knowledge is transferred during distillation, focusing on shared invariant outputs to counterfactual changes of dataset latent variables (we call these latents mechanisms). We define a student model to be a good stand-in model for a teacher if it shares the teacher’s learned mechanisms, and find that Jacobian matching and contrastive representation learning are viable methods by which to train such models. While these methods do not result in perfect transfer of mechanisms, we show they often improve student fidelity or mitigate simplicity bias (as measured by the teacher-to-student KL divergence and accuracy on various out-of-distribution test datasets), especially on datasets with spurious statistical correlations.
2024-05-14
Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models (published)
proceedings.mlr.press
openreview.net
Adaptive Exploration for Data-Efficient General Value Function Evaluations
Arushi Jain
Josiah P. Hanna
Doina Precup
General Value Functions (GVFs) (Sutton et al, 2011) are an established way to represent predictive knowledge in reinforcement learning. Each… (see more) GVF computes the expected return for a given policy, based on a unique pseudo-reward. Multiple GVFs can be estimated in parallel using off-policy learning from a single stream of data, often sourced from a fixed behavior policy or pre-collected dataset. This leaves an open question: how can behavior policy be chosen for data-efficient GVF learning? To address this gap, we propose GVFExplorer, which aims at learning a behavior policy that efficiently gathers data for evaluating multiple GVFs in parallel. This behavior policy selects actions in proportion to the total variance in the return across all GVFs, reducing the number of environmental interactions. To enable accurate variance estimation, we use a recently proposed temporal-difference-style variance estimator. We prove that each behavior policy update reduces the mean squared error in the summed predictions over all GVFs. We empirically demonstrate our method's performance in both tabular representations and nonlinear function approximation.
2024-05-13
ArXiv (preprint)
doi.org
arxiv.org
ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning
Qiao Gu
Alihusein Kuwajerwala
Sacha Morin
Krishna Murthy
Bipasha Sen
Aditya Agarwal
Corban Rivera
William Paul
Kirsty Ellis
Rama Chellappa
Chuang Gan
Celso M de Melo
Joshua B. Tenenbaum
Antonio Torralba
Florian Shkurti
Liam Paull
For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and effi… (see more)cient for task-driven perception and planning. Recent approaches have attempted to leverage features from large vision-language models to encode semantics in 3D representations. However, these approaches tend to produce maps with per-point feature vectors, which do not scale well in larger environments, nor do they contain semantic spatial relationships between entities in the environment, which are useful for downstream planning. In this work, we propose ConceptGraphs, an open-vocabulary graph-structured representation for 3D scenes. ConceptGraphs is built by leveraging 2D foundation models and fusing their output to 3D by multi-view association. The resulting representations generalize to novel semantic classes, without the need to collect large 3D datasets or finetune models. We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts. (Project page: https://concept-graphs.github.io/ Explainer video: https://youtu.be/mRhNkQwRYnc )
2024-05-13
2024 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
openreview.net
Divergent Creativity in Humans and Large Language Models
Antoine Bellemare-Pepin
Franccois Lespinasse
Philipp Thölke
Yann Harel
Kory Wallace Mathewson
Jay A. Olson
Yoshua Bengio
Karim Jerbi CoCo Lab
Psychology Department
U. Montr'eal
Montreal
Qc
Canada
Music department
C. University
Sociology
Anthropology department
Mila
Departmentof Psychology
University of Toronto Mississauga … (see 5 more)
Mississauga
On
Department of Computer Science
Operations Research
Unique Center
The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin … (see more)to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.
2024-05-13
ArXiv (preprint)
doi.org
arxiv.org