Unité

Chaire Fondation Bertarelli de Neuroscience Intégrative

Laboratoire

Publications associées (32)

The Unstoppable Rise of Computational Linguistics in Deep Learning

In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of v ...

Association for Computational Linguistics2020

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Mert Yüksekgönül

Deep neural networks are highly effective tools for human and animal pose estimation. However, robustness to out-of-domain data remains a challenge. Here, we probe the transfer and generalization ability for pose estimation with two architecture classes (M ...

2019

Biologically plausible deep learning – but how far can we go with shallow networks?

Wulfram Gerstner, Johanni Michael Brea, Bernd Albert Illing

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

2019

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Pranav Mamidanna

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be h ...

2018

Learning embeddings: efficient algorithms and applications

Cijo Jose

Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...

École Polytechnique Fédérale de Lausanne2018

Markerless tracking of user-defined features with deep learning

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Pranav Mamidanna

2018

Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine

Marc-Oliver Gewaltig

There have been many advances in the field of reinforcement learning in continuous control problems. Usually, these approaches use deep learning with artificial neural networks for approximation of policies and value functions. In addition, there have been ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

Learning embeddings: efficient algorithms and applications

Cijo Jose

EPFL2018

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

Word Sequence Modeling using Deep Learning

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

EPFL2016

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