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Incremental Learning Meets Reduced Precision Networks

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Multi-Level Error-Resilient Neural Networks

Amin Karbasi, Amir Hesam Salavati

The problem of neural network association is to retrieve a previously memorized pattern from its noisy version using a network of neurons. An ideal neural network should include three components simultaneously: a learning algorithm, a large pattern retriev ...

IEEE2012

Implementing Neural Networks Efficiently

Ronan Collobert

Neural networks and machine learning algorithms in general require a flexible environment where new algorithm prototypes and experiments can be set up as quickly as possible with best possible computational performance. To that end, we provide a new framew ...

Springer2012

Accelerating Thermal Simulations of 3D ICs with Liquid Cooling using Neural Networks

David Atienza Alonso, Arvind Raj Mahankali Sridhar, Martino Ruggiero, Alessandro Vincenzi

Vertical integration is a promising solution to further increase the performance of future ICs, but such 3D ICs present complex thermal issues that cannot be solved by conventional cooling techniques. Interlayer liquid cooling has been proposed to extract ...

ACM2012

MERIS observations of phytoplankton blooms in a stratified eutrophic lake

Alfred Johny Wüest

The use of spaceborne medium resolution imaging spectrometers with neural network algorithms has proven a large potential for application with optically complex inland waters. We make use of this approach to investigate the bio-physical dynamics in a eutro ...

Elsevier Science Inc2012

Exponential Pattern Retrieval Capacity with Non-Binary Associative Memory

Mohammad Amin Shokrollahi, Krishna Kumar, Amir Hesam Salavati

We consider the problem of neural association for a network of non-binary neurons. Here, the task is to recall a previously memorized pattern from its noisy version using a network of neurons whose states assume values from a finite number of non-negative ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

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

Neural conditional random fields

We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we ap ...

JMLR: W&CP2010

Neural Network based Regression for Robust Overlapping Speech Recognition using Microphone Arrays

Hervé Bourlard, John David Scott Dines, Weifeng Li

This paper investigates a neural network based acoustic feature mapping to extract robust features for automatic speech recognition (ASR) of overlapping speech. In our preliminary studies, we trained neural networks to learn the mapping from log mel filter ...

IDIAP2008

Supervised Learning from the Bayesian Viewpoint: An informal overview

Claudio Mattiussi

This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probability. Its aim is showing in very informal terms how supervised learning can be interpreted from the Bayesian viewpoint. The focus is put on supervised learning ...

2006

Improving Face Authetication Using Virtual Samples

Sébastien Marcel, Samy Bengio, Norman Hoon Thian Poh

In this paper, we present a simple yet effective way to improve a face verification system by generating multiple virtual samples from the unique image corresponding to an access request. These images are generated using simple geometric transformations. T ...

2003