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The inductive bias of deep learning: Connecting weights and functions

Publications associées (43)

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Deep Micro-Dictionary Learning and Coding Network

Yan Yan, Wei Wang, Wei Xiao

In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional l ...

IEEE2019

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Learning Latent Representations of 3D Human Pose with Deep Neural Networks

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Bugra Tekin

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from an image to a 3D pose, which ignores the dependencies between human joints, or model these dependencies ...

2018

Learning Aerial Image Segmentation from Online Maps

Martin Jaggi, Jan Dirk Wegner, Aurélien Lucchi, Thomas Hofmann

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) ...

Institute of Electrical and Electronics Engineers2017

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

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

Structured Prediction of 3D Human Pose with Deep Neural Networks

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Vincent Lepetit, Bugra Tekin

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a ...

2016

Microstructure informed tractography: pitfalls and open challenges

Jean-Philippe Thiran, Alessandro Daducci, Maxime Descoteaux

One of the major limitations of diffusion MRI tractography is that the fiber tracts recovered by existing algorithms are not truly quantitative. Local techniques for estimating more quantitative features of the tissue microstructure exist, but their combin ...

Frontiers Research Foundation2016

A Deeper Look at Dataset Bias

Barbara Caputo, Tatiana Tommasi, Novi Patricia

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid deve ...

Springer Int Publishing Ag2015

Stochastic Spectral Descent for Restricted Boltzmann Machines.

Volkan Cevher

Restricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Learning typically proceeds by using stochastic gradient descent, and the gradients are estimated with sampling methods. However, the gradient estimation is a ...

2015