Publications

Revisiting Feature Prediction for Learning Visual Representations from Video
Adrien Bardes
Quentin Garrido
Jean Ponce
Xinlei Chen
Michael Rabbat
Yann LeCun
Mahmoud Assran
Nicolas Ballas
2024-08-09
TMLR (accepté)
doi.org
openreview.net
scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information
Yingfu Wu
Zhenqi Shi
Xiangfei Zhou
Pengyu Zhang
Xiuhui Yang
Jun Ding
Hao Wu
2024-07-31
Communications Biology (publié)
doi.org
Strong gravitational lensing as a probe of dark matter
Simona Vegetti
Simon Birrer
Giulia Despali
C. Fassnacht
Daniel A. Gilman
Yashar Hezaveh
L.
Laurence Perreault-Levasseur
J. McKean
D. Powell
Conor M. O'riordan
G.
Vernardos
Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple… (voir plus) images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models in a way that is independent of the baryonic content of matter structures on subgalactic scales. In this chapter, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide a historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the near future. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution follow-up observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing.
2024-07-30
Space Science Reviews (publié)
doi.org
arxiv.org
Long-term plasticity induces sparse and specific synaptic changes in a biophysically detailed cortical model
András Ecker
Daniela Egas Santander
Marwan Abdellah
Jorge Blanco Alonso
Sirio Bolaños-Puchet
Giuseppe Chindemi
Dhuruva Priyan Gowri Mariyappan
James B. Isbister
James King
Pramod Kumbhar
Ioannis Magkanaris
Eilif Benjamin Muller
Michael W. Reimann
2024-07-29
bioRxiv (prépublication)
doi.org
scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico Exploration
Xiuhui Yang
Koren K. Mann
Hao Wu
Jun Ding
Single-cell multi-omics illuminate intricate cellular states, yielding transformative insights into cellular dynamics and disease. Yet, whil… (voir plus)e the potential of this technology is vast, the integration of its multifaceted data presents challenges. Some modalities have not reached the robustness or clarity of established scRNA-seq. Coupled with data scarcity for newer modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross: a tool adeptly engineered using variational autoencoder, generative adversarial network principles, and the Mutual Nearest Neighbors (MNN) technique for modality alignment. This synergy ensures seamless integration of varied single-cell multi-omics data. Beyond its foundational prowess in multi-omics data integration, scCross excels in single-cell cross-modal data generation, multi-omics data simulation, and profound in-silico cellular perturbations. Armed with these capabilities, scCross is set to transform the field of single-cell research, establishing itself in the nuanced integration, generation, and simulation of complex multi-omics data.
2024-07-29
Genome Biology (publié)
doi.org
Canada's Provincial Covid-19 Pandemic Modelling Efforts: A Review of Mathematical Models and Their Impacts on the Responses
Yiqing Xia
Jorge Luis Flores Anato
Caroline Colijin
Naveed Janjua
Michael Otterstatter
Mike Irvine
Tyler Williamson
Marie B. Varughese
Michael Li
Nathaniel Osgood
David J. D. Earn
Beate Sander
Lauren E. Cipriano
Kumar Murty
Fanyu Xiu
Arnaud Godin
David Buckeridge
Amy Hurford
Sharmistha Mishra
Mathieu Maheu-Giroux
2024-07-25
Canadian Journal of Public Health (publié)
doi.org
Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
Ziyan Luo
Xujie Si
Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a wide range of verification and analysis tasks. Data-driven appro… (voir plus)aches show great promise in improving CHC solving without the painstaking manual effort of creating and tuning various heuristics. However, a large performance gap exists between data-driven CHC solvers and symbolic reasoning-based solvers. In this work, we develop a simple but effective framework,"Chronosymbolic Learning", which unifies symbolic information and numerical data points to solve a CHC system efficiently. We also present a simple instance of Chronosymbolic Learning with a data-driven learner and a BMC-styled reasoner. Despite its great simplicity, experimental results show the efficacy and robustness of our tool. It outperforms state-of-the-art CHC solvers on a dataset consisting of 288 benchmarks, including many instances with non-linear integer arithmetics.
2024-07-17
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
scSemiProfiler: Advancing Large-scale Single-cell Studies through Semi-profiling with Deep Generative Models and Active Learning
Jingtao Wang
Gregory Fonseca
Jun Ding
2024-07-16
Nature Communications (publié)
doi.org
Group Membership Bias
Ali Vardasbi
Maarten de Rijke
Fernando Diaz
Mostafa Dehghani
2024-07-11
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (publié)
doi.org
arxiv.org
All-in-one simulation-based inference
Manuel Gloeckler
Michael Deistler
Christian Dietrich Weilbach
Frank Wood
Jakob H. Macke
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
CKGConv: General Graph Convolution with Continuous Kernels
Liheng Ma
Soumyasundar Pal
Yitian Zhang
Jiaming Zhou
Yingxue Zhang
Mark Coates
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a gene… (voir plus)ral convolution operator in the graph domain is challenging due to the lack of canonical coordinates, the presence of irregular structures, and the properties of graph symmetries. In this work, we propose a novel and general graph convolution framework by parameterizing the kernels as continuous functions of pseudo-coordinates derived via graph positional encoding. We name this Continuous Kernel Graph Convolution (CKGConv). Theoretically, we demonstrate that CKGConv is flexible and expressive. CKGConv encompasses many existing graph convolutions, and exhibits a stronger expressiveness, as powerful as graph transformers in terms of distinguishing non-isomorphic graphs. Empirically, we show that CKGConv-based Networks outperform existing graph convolutional networks and perform comparably to the best graph transformers across a variety of graph datasets. The code and models are publicly available at https://github.com/networkslab/CKGConv.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
Daniel D. Johnson
Danny Tarlow
David Duvenaud
Chris J. Maddison
Identifying how much a model …
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net