Publications

Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection
Eduardo Dadalto Câmara Gomes
Florence Alberge
Pierre Duhamel
Pablo Piantanida
2023-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study
Mohammad Mehdi Morovati
Florian Tambon
Mina Taraghi
Amin Nikanjam
Foutse Khomh
Machine Learning (ML) is increasingly being adopted in different industries. Deep Reinforcement Learning (DRL) is a subdomain of ML used to … (voir plus)produce intelligent agents. Despite recent developments in DRL technology, the main challenges that developers face in the development of DRL applications are still unknown. To fill this gap, in this paper, we conduct a large-scale empirical study of 927 DRL-related posts extracted from Stack Overflow, the most popular Q&A platform in the software community. Through the process of labeling and categorizing extracted posts, we created a taxonomy of common challenges encountered in the development of DRL applications, along with their corresponding popularity levels. This taxonomy has been validated through a survey involving 65 DRL developers. Results show that at least 45% of developers experienced 18 of the 21 challenges identified in the taxonomy. The most frequent source of difficulty during the development of DRL applications are Comprehension, API usage, and Design problems, while Parallel processing, and DRL libraries/frameworks are classified as the most difficult challenges to address, with respect to the time required to receive an accepted answer. We hope that the research community will leverage this taxonomy to develop efficient strategies to address the identified challenges and improve the quality of DRL applications.
2023-12-31
Empir. Softw. Eng. (publié)
doi.org
arxiv.org
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning
Tianyu Li
Doina Precup
Guillaume Rabusseau
In this paper, we present connections between three models used in different research fields: weighted finite automata~(WFA) from formal lan… (voir plus)guages and linguistics, recurrent neural networks used in machine learning, and tensor networks which encompasses a set of optimization techniques for high-order tensors used in quantum physics and numerical analysis. We first present an intrinsic relation between WFA and the tensor train decomposition, a particular form of tensor network. This relation allows us to exhibit a novel low rank structure of the Hankel matrix of a function computed by a WFA and to design an efficient spectral learning algorithm leveraging this structure to scale the algorithm up to very large Hankel matrices.We then unravel a fundamental connection between WFA and second-orderrecurrent neural networks~(2-RNN): in the case of sequences of discrete symbols, WFA and 2-RNN with linear activationfunctions are expressively equivalent. Leveraging this equivalence result combined with the classical spectral learning algorithm for weighted automata, we introduce the first provable learning algorithm for linear 2-RNN defined over sequences of continuous input vectors.This algorithm relies on estimating low rank sub-blocks of the Hankel tensor, from which the parameters of a linear 2-RNN can be provably recovered. The performances of the proposed learning algorithm are assessed in a simulation study on both synthetic and real-world data.
2023-12-31
Mach. Learn. (publié)
doi.org
arxiv.org
Consolidating Separate Degradations Model via Weights Fusion and Distillation
Dinesh Daultani
Hugo Larochelle
Real-world images prevalently contain different varieties of degradation, such as motion blur and luminance noise. Computer vision recogniti… (voir plus)on models trained on clean images perform poorly on degraded images. Previously, several works have explored how to perform image classification of degraded images while training a single model for each degradation. Nevertheless, it becomes challenging to host several degradation models for each degradation on limited hardware applications and to estimate degradation parameters correctly at the run-time. This work proposes a method for effectively combining several models trained separately on different degradations into a single model to classify images with different types of degradations. Our proposed method is four-fold: (1) train a base model on clean images, (2) fine-tune the base model in-dividually for all given image degradations, (3) perform a fusion of weights given the fine-tuned models for individual degradations, (4) perform fine-tuning on given task using distillation and cross-entropy loss. Our proposed method can outperform previous state-of-the-art methods of pretraining in out-of-distribution generalization based on degradations such as JPEG compression, salt-and-pepper noise, Gaussian blur, and additive white Gaussian noise by 2.5% on CIFAR-100 dataset and by 1.3% on CIFAR-10 dataset. Moreover, our proposed method can handle degra-dation used for training without any explicit information about degradation at the inference time. Code will be available at https://github.com/dineshdaultani/FusionDistill.
2023-12-31
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (publié)
doi.org
Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models
Jerry Huang
Prasanna Parthasarathi
Mehdi Rezagholizadeh
A. Chandar
2023-12-31
EMNLP (publié)
doi.org
arxiv.org
Corticosteroids induce an early but limited decrease in IL-6 dependent pro-inflammatory responses in critically ill COVID-19 patients
Tomas URBINA
Paul GABARRE
Vincent BONNY
Jean-Rémi Lavillegrand
Marc GARNIER
Jérémie JOFFRE
Nathalie MARIO
Guillaume Dumas
Geoffroy HARIRI
Matthieu TURPIN
Emmanuel PARDO
Muriel FARTOUKH
Bertrand GUIDET
Eric Maury
Yannick CHANTRAN
Pierre-Yves BOELLE
Guillaume VOIRIOT
Hafid AIT-OUFELLA
2023-12-31
Minerva Anestesiologica (publié)
doi.org
Dance of the Neurons: Unraveling Sex from Brain Signals (short paper).
Mohammad-Javad Darvishi Bayazi
Mohammad Sajjad Ghaemi
Jocelyn Faubert
Irina Rish
2023-12-31
ML4CMH@AAAI (publié)
dblp.uni-trier.de
Data-access performance anti-patterns in data-intensive systems
Biruk Asmare Muse
Kawser Wazed Nafi
Foutse Khomh
Giuliano Antoniol
Data-intensive systems handle variable, high volume, and high-velocity data generated by human and digital devices. Like traditional softwar… (voir plus)e, data-intensive systems are prone to technical debts introduced to cope-up with the pressure of time and resource constraints on developers. Data-access is a critical component of data-intensive systems as it determines the overall performance and functionality of such systems. While data access technical debts are getting attention from the research community, technical debts affecting the performance, are not well investigated. Objective: Identify, categorize, and validate data access performance issues in the context of NoSQL-based and polyglot persistence data-intensive systems using qualitative study. Method: We collect issues from NoSQL-based and polyglot persistence open-source data-intensive systems and identify data access performance issues using inductive coding and build a taxonomy of the root causes. Then, we validate the perceived relevance of the newly identified performance issues using a developer survey.
2023-12-31
Empir. Softw. Eng. (publié)
doi.org
arxiv.org
Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP
Chen Su
William A. Pastor
Amin Emad
Deciphering the underlying gene regulatory networks (GRNs) that govern early human embryogenesis is critical for understanding developmental… (voir plus) mechanisms yet remains challenging due to limited sample availability and the inherent complexity of the biological processes involved. To address this, we developed InPheRNo-ChIP, a computational framework that integrates multimodal data, including RNA-seq, transcription factor (TF)–specific ChIP-seq, and phenotypic labels, to reconstruct phenotype-relevant GRNs associated with endoderm development. The core of this method is a probabilistic graphical model that models the simultaneous effect of TFs on their putative target genes to influence a particular phenotypic outcome. Unlike the majority of existing GRN inference methods that are agnostic to the phenotypic outcomes, InPheRNo-ChIP directly incorporates phenotypic information during GRN inference, enabling the distinction between lineage-specific and general regulatory interactions. We integrated data from three experimental studies and applied InPheRNo-ChIP to infer the GRN governing the differentiation of human embryonic stem cells into definitive endoderm. Benchmarking against a scRNA-seq CRISPRi study demonstrated InPheRNo-ChIP’s ability to identify regulatory interactions involving endoderm markers FOXA2, SMAD2, and SOX17, outperforming other methods. This highlights the importance of incorporating the phenotypic context during network inference. Furthermore, an ablation study confirms the synergistic contribution of ChIP-seq, RNA-seq, and phenotypic data, highlighting the value of multimodal integration for accurate phenotype-relevant GRN reconstruction.
2023-12-31
Briefings Bioinform. (publié)
doi.org
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Nima Fathi
Amar Kumar
Brennan Nichyporuk
Mohammad Havaei
Tal Arbel
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (voir plus)with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.
2023-12-31
MIDL (publié)
doi.org
openreview.net
Decoding of Polar Codes Using Quadratic Unconstrained Binary Optimization
Huayi Zhou
Ryan Seah
Marwan Jalaleddine
Warren J. Gross
2023-12-31
IEEE Journal on Selected Areas in Communications (publié)
doi.org
Deep Learning Approach for Changepoint Detection: Penalty Parameter Optimization
Tung L. Nguyen
Toby Dylan Hocking
Changepoint detection, a technique for identifying significant shifts within data sequences, is crucial in various fields such as finance, g… (voir plus)enomics, medicine, etc. Dynamic programming changepoint detection algorithms are employed to identify the locations of changepoints within a sequence, which rely on a penalty parameter to regulate the number of changepoints. To estimate this penalty parameter, previous work uses simple models such as linear models or decision trees. This study introduces a novel deep learning method for predicting penalty parameters, leading to demonstrably improved changepoint detection accuracy on large benchmark supervised labeled datasets compared to previous methods.
2023-12-31
arXiv.org (prépublication)
doi.org