Publications

Reference panel-guided super-resolution inference of Hi-C data
Yanlin Zhang
Mathieu Blanchette
Abstract Motivation Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding… (voir plus) the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types. Results Here, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains. Availability and implementation https://github.com/BlanchetteLab/RefHiC.
2023-06-30
Bioinformatics (publié)
doi.org
Should We Feed the Trolls? Using Marketer-Generated Content to Explain Average Toxicity and Product Usage
Marcelo Vinhal Nepomuceno
Hooman Rahemi
Tolga Cenesizoglu
Laurent Charlin
2023-06-29
Journal of Interactive Marketing (publié)
doi.org
Cortico-Cerebellar neurodynamics during social interaction in Autism Spectrum Disorders
Fleur Gaudfernau
Aline Lefebvre
Denis-Alexander Engemann
Amandine Pedoux
Anna Bánki
Florence Baillin
Benjamin Landman
Frederique Amsellem
Anna Maruani
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
2023-06-28
NeuroImage : Clinical (publié)
doi.org
On the Identifiability of Quantized Factors
Vitória Barin Pacela
Kartik Ahuja
Simon Lacoste-Julien
Pascal Vincent
Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the … (voir plus)theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.
2023-06-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines
Alexandre Adam
Laurence Perreault-Levasseur
Yashar Hezaveh
Max Welling
Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass densit… (voir plus)y in foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the recurrent inference machine to reconstruct simultaneously an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation.
2023-06-27
The Astrophysical Journal (publié)
doi.org
arxiv.org
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Dianbo Liu
Alex Lamb
Xu Ji
Pascal Notsawo
Michael Curtis Mozer
Yoshua Bengio
Kenji Kawaguchi
2023-06-26
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
Sahand Rezaei-Shoshtari
Charlotte Morissette
Francois Hogan
Gregory Dudek
David Meger
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objec… (voir plus)tive and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
2023-06-26
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)
Charles Bourbeau
Audrey Durand
This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that… (voir plus) stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.
2023-06-26
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Signed Laplacian Graph Neural Networks
Yu Li
Meng Qu
Jian Tang
Yi Chang
2023-06-26
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
The Effect of diversity in Meta-Learning
Ramnath Kumar
Tristan Deleu
Yoshua Bengio
Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that task … (voir plus)distribution plays a vital role in the performance of the model. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; we study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms. For this experiment, we train on multiple datasets, and with three broad classes of meta-learning models - Metric-based (i.e., Protonet, Matching Networks), Optimization-based (i.e., MAML, Reptile, and MetaOptNet), and Bayesian meta-learning models (i.e., CNAPs). Our experiments demonstrate that the effect of task diversity on all these algorithms follows a similar trend, and task diversity does not seem to offer any benefits to the learning of the model. Furthermore, we also demonstrate that even a handful of tasks, repeated over multiple batches, would be sufficient to achieve a performance similar to uniform sampling and draws into question the need for additional tasks to create better models.
2023-06-26
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
openreview.net
Partial Ordered Statistics Decoding with Enhanced Error Patterns
Marwan Jalaleddine
Huayi Zhou
Jiajie Li
Warren Gross
Guessing Random Additive Noise Decoding (GRAND) excels at decoding high-rate codes but struggles to decode low-rate codes with reasonable co… (voir plus)mplexity. Ordered Statistics Decoding (OSD) specifically excels in decoding short codes irrespective of rates; however, OSD necessitates the use of Gaussian elimination which introduces additional time, space and computational complexity. Partial Ordered Statistics Decoding (POSD) was proposed to reduce the time, space, and computational complexity of OSD; however, the current partition-based POSD has poor decoding performance since it does not generate test error patterns across partitions. In this paper, we propose to improve the decoding performance of POSD by incorporating test error patterns inspired by GRAND methods. This work offers a trade-off between performance and complexity compared to existing decoders such as GRAND and OSD. We enhance POSD by optimizing the scheduling of Test Error Patterns (TEPs) and show that our technique can be applied to any code in a standard form. At a target BER 10−4 with eBCH (128,64) the enhanced error patterns achieve more than 0.6 dB gain in performance compared to the POSD with partition-based error patterns. Moreover, at a target frame error rate of 10−5, POSD uses 10× less binary operations compared to GRAND when decoding eBCH (128,64) and RLC(128,64) codes. With BCH (127,29) and RLC(128,32), at a target frame error rate of 10−2, POSD with enhanced error patterns with a maximum number of queries (MQ) of 104 achieves up to a 2 dB gain to its GRAND equivalent which is using 107 maximum number of queries.
2023-06-25
2023 IEEE International Symposium on Information Theory (ISIT) (publié)
doi.org
CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation
Amal Feriani
Di Wu
Steve Liu
Gregory Dudek
2023-06-23
ArXiv (prépublication)
doi.org
arxiv.org