François Fleuret

research

∙ 03/07/2022

HyperMixer: An MLP-based Green AI Alternative to Transformers

Transformer-based architectures are the model of choice for natural lang...

Florian Mai, et al. ∙

research

∙ 03/02/2022

The Theoretical Expressiveness of Maxpooling

Over the decade since deep neural networks became state of the art image...

Kyle Matoba, et al. ∙

research

∙ 02/17/2022

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

Reinforcement learning competitions advance the field by providing appro...

Anssi Kanervisto, et al. ∙

research

∙ 02/11/2022

Borrowing from yourself: Faster future video segmentation with partial channel update

Semantic segmentation is a well-addressed topic in the computer vision l...

Evann Courdier, et al. ∙

research

∙ 02/10/2022

SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics

Deep learning methods have gained popularity in high energy physics for ...

Atul Kumar Sinha, et al. ∙

research

∙ 02/09/2022

Agree to Disagree: Diversity through Disagreement for Better Transferability

Gradient-based learning algorithms have an implicit simplicity bias whic...

Matteo Pagliardini, et al. ∙

research

∙ 12/20/2021

Efficient Wind Speed Nowcasting with GPU-Accelerated Nearest Neighbors Algorithm

This paper proposes a simple yet efficient high-altitude wind nowcasting...

Arnaud Pannatier, et al. ∙

research

∙ 11/26/2021

GeoNeRF: Generalizing NeRF with Geometry Priors

We present GeoNeRF, a generalizable photorealistic novel view synthesis ...

Mohammad Mahdi Johari, et al. ∙

research

∙ 10/19/2021

Test time Adaptation through Perturbation Robustness

Data samples generated by several real world processes are dynamic in na...

Prabhu Teja Sivaprasad, et al. ∙

research

∙ 09/08/2021

Priming PCA with EigenGame

We introduce primed-PCA (pPCA), an extension of the recently proposed Ei...

Bálint Máté, et al. ∙

research

∙ 04/16/2021

Language Models are Few-Shot Butlers

Pretrained language models demonstrate strong performance in most NLP ta...

Vincent Micheli, et al. ∙

research

∙ 04/13/2021

Structural analysis of an all-purpose question answering model

Attention is a key component of the now ubiquitous pre-trained language ...

Vincent Micheli, et al. ∙

research

∙ 01/26/2021

Unsupervised clustering of series using dynamic programming and neural processes

Following the work of arXiv:2101.09512, we are interested in clustering ...

Karthigan Sinnathamby, et al. ∙

research

∙ 01/23/2021

Unsupervised clustering of series using dynamic programming

We are interested in clustering parts of a given single multi-variate se...

Karthigan Sinnathamby, et al. ∙

research

∙ 10/08/2020

On the importance of pre-training data volume for compact language models

Recent advances in language modeling have led to computationally intensi...

Vincent Micheli, et al. ∙

research

∙ 07/09/2020

Fast Transformers with Clustered Attention

Transformers have been proven a successful model for a variety of tasks ...

Apoorv Vyas, et al. ∙

research

∙ 06/29/2020

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Transformers achieve remarkable performance in several tasks but due to ...

Angelos Katharopoulos, et al. ∙

research

∙ 06/25/2020

Taming GANs with Lookahead

Generative Adversarial Networks are notoriously challenging to train. Th...

Tatjana Chavdarova, et al. ∙

research

∙ 06/16/2020

Gradient Alignment in Deep Neural Networks

One cornerstone of interpretable deep learning is the high degree of vis...

Suraj Srinivas, et al. ∙

research

∙ 04/06/2020

Fair Latency-Aware Metric for real-time video segmentation networks

As supervised semantic segmentation is reaching satisfying results, many...

Evann Courdier, et al. ∙

research

∙ 02/08/2020

Multi-task Reinforcement Learning with a Planning Quasi-Metric

We introduce a new reinforcement learning approach combining a planning ...

Vincent Micheli, et al. ∙

research

∙ 10/25/2019

On the Tunability of Optimizers in Deep Learning

There is no consensus yet on the question whether adaptive gradient meth...

Prabhu Teja Sivaprasad, et al. ∙

research

∙ 05/03/2019

Processing Megapixel Images with Deep Attention-Sampling Models

Existing deep architectures cannot operate on very large signals such as...

Angelos Katharopoulos, et al. ∙

research

∙ 05/02/2019

Full-Jacobian Representation of Neural Networks

Non-linear functions such as neural networks can be locally approximated...

Suraj Srinivas, et al. ∙

research

∙ 04/18/2019

Reducing Noise in GAN Training with Variance Reduced Extragradient

Using large mini-batches when training generative adversarial networks (...

Tatjana Chavdarova, et al. ∙

research

∙ 06/05/2018

Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching

End-to-end deep-learning networks recently demonstrated extremely good p...

Stepan Tulyakov, et al. ∙

research

∙ 03/02/2018

Not All Samples Are Created Equal: Deep Learning with Importance Sampling

Deep neural network training spends most of the computation on examples ...

Angelos Katharopoulos, et al. ∙

research

∙ 03/01/2018

Knowledge Transfer with Jacobian Matching

Classical distillation methods transfer representations from a "teacher"...

Suraj Srinivas, et al. ∙

research

∙ 02/12/2018

Geodesic Convolutional Shape Optimization

Aerodynamic shape optimization has many industrial applications. Existin...

Pierre Baqué, et al. ∙

research

∙ 12/06/2017

SGAN: An Alternative Training of Generative Adversarial Networks

The Generative Adversarial Networks (GANs) have demonstrated impressive ...

Tatjana Chavdarova, et al. ∙

research

∙ 07/28/2017

The WILDTRACK Multi-Camera Person Dataset

People detection methods are highly sensitive to the perpetual occlusion...

Tatjana Chavdarova, et al. ∙

research

∙ 07/03/2017

Geometric calibration of Colour and Stereo Surface Imaging System of ESA's Trace Gas Orbiter

There are many geometric calibration methods for "standard" cameras. The...

Stepan Tulyakov, et al. ∙

research

∙ 12/03/2016

Semi-supervised learning of deep metrics for stereo reconstruction

Deep-learning metrics have recently demonstrated extremely good performa...

Stepan Tulyakov, et al. ∙

research

∙ 12/02/2016

Globally Consistent Multi-People Tracking using Motion Patterns

Many state-of-the-art approaches to people tracking rely on detecting th...

Andrii Maksai, et al. ∙

research

∙ 11/28/2016

Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition

We present a unified framework for understanding human social behaviors ...

Timur Bagautdinov, et al. ∙

research

∙ 11/23/2016

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Mean Field inference is central to statistical physics. It has attracted...

Pierre Baqué, et al. ∙

research

∙ 10/13/2016

Predicting the dynamics of 2d objects with a deep residual network

We investigate how a residual network can learn to predict the dynamics ...

François Fleuret, et al. ∙

research

∙ 05/23/2016

A Sub-Quadratic Exact Medoid Algorithm

We present a new algorithm, trimed, for obtaining the medoid of a set, t...

James Newling, et al. ∙

research

∙ 03/01/2016

Scalable Metric Learning via Weighted Approximate Rank Component Analysis

We are interested in the large-scale learning of Mahalanobis distances, ...

Cijo Jose, et al. ∙

research

∙ 02/09/2016

Nested Mini-Batch K-Means

A new algorithm is proposed which accelerates the mini-batch k-means alg...

James Newling, et al. ∙

research

∙ 02/08/2016

Fast K-Means with Accurate Bounds

We propose a novel accelerated exact k-means algorithm, which performs b...

James Newling, et al. ∙

François Fleuret

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