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Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

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Exploring Feature Dimensions to Learn a New Policy in an Uninformed Reinforcement Learning Task

Oh-Hyeon Choung

When making a choice with limited information, we explore new features through trial-and-error to learn how they are related. However, few studies have investigated exploratory behaviour when information is limited. In this study, we address, at both the b ...

Springer Nature2017

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

Deep(ly) Learning the Depth

Xun Victor Suo

We report on the use of deep learning algorithms to perform depth recovery in multiview imaging. We show that if enough training data are provided, a neural network such as multilayer perceptron can be trained to recover the depth in multiview imaging as a ...

2016

Numerical modeling and neural networks to identify model parameters from piezocone tests: II. Multi-parameter identification from piezocone data

Laurent Vulliet, Rafal Filip Obrzud, Andrzej Truty

This paper completes the study presented in the accompanying paper, and demonstrates a numerical algorithm for parameter prediction from the piezocone test (CPTU) data. This part deals with a development of neural network (NN) models which are able to map ...

Wiley-Blackwell2012

PyBrain

Yi Sun, Daniël Pieter Wierstra, Tom Schaul

PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easy-to-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. ...

Massachusetts Institute of Technology Press2010

Application of neuro-fuzzy approach in prediction of runup in swash zone

Roham Bakhtyar

A prominent parameter in dealing with swash and morphological evolution is the runup length or height, defined as the limit of landward sea. Therefore, it is necessary to predict the runup height in this area. In this paper, the abilities of a new Adaptive ...

2008

Hierarchical Neural Networks Feature Extraction for LVCSR system

Hynek Hermansky, Fabio Valente

This paper investigates the use of a hierarchy of Neural Networks for performing data driven feature extraction. Two different hierarchical structures based on long and short temporal context are considered. Features are tested on two different LVCSR syste ...

2007

Hierarchical Neural Networks Feature Extraction for LVCSR system

Hynek Hermansky, Fabio Valente

IDIAP2007

Extended Hopfield Network for Sequence Learning: Application to Gesture Recognition

Aude Billard, Micha Hersch, André Maurer

In this paper, we extend the Hopfield Associative Memory for storing multiple sequences of varying duration. We apply the model for learning, recognizing and encoding a set of human gestures. We measure systematically the performance of the model against n ...

2005

Pruning of Neural Networks

Choosing a suitable topology for a neural network, given an application, is a difficult problem. Usually, after a tedious trial-and-error process, an oversized topology is chosen, which is prone to various drawbacks like a high demand on computational reso ...

IDIAP1997