Search Results for author: Samira Ebrahimi Kahou

Found 32 papers, 13 papers with code

Spectral Temporal Contrastive Learning

no code implementations1 Dec 2023 Sacha Morin, Somjit Nath, Samira Ebrahimi Kahou, Guy Wolf

This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts.

Contrastive Learning Self-Supervised Learning

Fairness Under Demographic Scarce Regime

1 code implementation24 Jul 2023 Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji

Surprisingly, our framework outperforms models trained with constraints on the true sensitive attributes.

Attribute Fairness

Transformers in Reinforcement Learning: A Survey

no code implementations12 Jul 2023 Pranav Agarwal, Aamer Abdul Rahman, Pierre-Luc St-Charles, Simon J. D. Prince, Samira Ebrahimi Kahou

We present a broad overview of how transformers have been adapted for several applications, including robotics, medicine, language modeling, cloud computing, and combinatorial optimization.

Cloud Computing Combinatorial Optimization +4

CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning

1 code implementation19 Jun 2023 Nikunj Gupta, Somjit Nath, Samira Ebrahimi Kahou

Before taking actions in an environment with more than one intelligent agent, an autonomous agent may benefit from reasoning about the other agents and utilizing a notion of a guarantee or confidence about the behavior of the system.

Conformal Prediction Decision Making +2

Discovering Object-Centric Generalized Value Functions From Pixels

1 code implementation27 Apr 2023 Somjit Nath, Gopeshh Raaj Subbaraj, Khimya Khetarpal, Samira Ebrahimi Kahou

Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using hand-crafted auxiliary tasks and pseudo rewards.

Object

Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies

no code implementations15 Dec 2022 Shivakanth Sujit, Pedro H. M. Braga, Jorg Bornschein, Samira Ebrahimi Kahou

Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning.

Offline RL reinforcement-learning +1

Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning

no code implementations28 Nov 2022 Ivaxi Sheth, Aamer Abdul Rahman, Mohammad Havaei, Samira Ebrahimi Kahou

Despite the boost in performance observed by using CBN layers, our work reveals that the visual features learned by introducing auxiliary data via CBN deteriorates.

Cancer type classification

Automatic Evaluation of Excavator Operators using Learned Reward Functions

1 code implementation15 Nov 2022 Pranav Agarwal, Marek Teichmann, Sheldon Andrews, Samira Ebrahimi Kahou

Training novice users to operate an excavator for learning different skills requires the presence of expert teachers.

Learning Latent Structural Causal Models

no code implementations24 Oct 2022 Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions.

Bayesian Inference Image Generation +1

Locally Constrained Representations in Reinforcement Learning

no code implementations20 Sep 2022 Somjit Nath, Rushiv Arora, Samira Ebrahimi Kahou

This encourages the representations to be driven not only by the value/policy learning but also by an additional loss that constrains the representations from over-fitting to the value loss.

Continuous Control reinforcement-learning +2

FHIST: A Benchmark for Few-shot Classification of Histological Images

no code implementations31 May 2022 Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi, Ivaxi Sheth, Mohammad Havaei, Ismail Ben Ayed, Samira Ebrahimi Kahou

We build few-shot tasks and base-training data with various tissue types, different levels of domain shifts stemming from various cancer sites, and different class-granularity levels, thereby reflecting realistic scenarios.

Classification Few-Shot Learning +1

Simple Video Generation using Neural ODEs

no code implementations7 Sep 2021 David Kanaa, Vikram Voleti, Samira Ebrahimi Kahou, Christopher Pal

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely challenging.

Video Generation

Accounting for Variance in Machine Learning Benchmarks

no code implementations1 Mar 2021 Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.

Benchmarking BIG-bench Machine Learning +1

Predicting Regional Locust Swarm Distribution with Recurrent Neural Networks

no code implementations29 Nov 2020 Hadia Mohmmed Osman Ahmed Samil, Annabelle Martin, Arnav Kumar Jain, Susan Amin, Samira Ebrahimi Kahou

Locust infestation of some regions in the world, including Africa, Asia and Middle East has become a concerning issue that can affect the health and the lives of millions of people.

ChatPainter: Improving Text to Image Generation using Dialogue

no code implementations22 Feb 2018 Shikhar Sharma, Dendi Suhubdy, Vincent Michalski, Samira Ebrahimi Kahou, Yoshua Bengio

Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging task.

Ranked #25 on Text-to-Image Generation on MS COCO (Inception score metric)

Text-to-Image Generation

ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

1 code implementation NeurIPS 2017 Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal

We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.

BIG-bench Machine Learning Blocking +1

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

BIG-bench Machine Learning Clustering +2

RATM: Recurrent Attentive Tracking Model

no code implementations29 Oct 2015 Samira Ebrahimi Kahou, Vincent Michalski, Roland Memisevic

The proposed Recurrent Attentive Tracking Model performs well on all three tasks and can generalize to related but previously unseen sequences from a challenging tracking data set.

Object Tracking

FitNets: Hints for Thin Deep Nets

3 code implementations19 Dec 2014 Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio

In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student.

Knowledge Distillation

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