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Concept

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Publications associées (28)

Multilingual Training and Adaptation in Speech Recognition

Sibo Tong

State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require thousands of hours of transcribed speech data during training. Therefore, building multilingual ...

EPFL2020

Learning from demonstration for semi-autonomous teleoperation

Sylvain Calinon

Teleoperation in domains such as deep-sea or space often requires the completion of a set of recurrent tasks. We present a framework that uses a probabilistic approach to learn from demonstration models of manipulation tasks. We show how such a framework c ...

2019

Untangling emergent cortical dynamics: neurons from networks, noise from chaos

Max Christian Nolte

The way in which cortical microcircuit components -- most importantly neurons -- and their connectivity -- the network -- shape and constrain emergent dynamics is a long-standing question in neuroscience. Experimentally observed dynamical properties can of ...

EPFL2019

Cortical reliability amid noise and chaos

Henry Markram, Eilif Benjamin Muller, Michael Reimann, James Gonzalo King, Max Christian Nolte

Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potentia ...

Nature Research2019

Temporal Spiking Recurrent Neural Network for Action Recognition

Wei Wang

In this paper, we propose a novel temporal spiking recurrent neural network (TSRNN) to perform robust action recognition in videos. The proposed TSRNN employs a novel spiking architecture which utilizes the local discriminative features from high-confidenc ...

2019

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Wulfram Gerstner, Tilo Schwalger, Samuel Pavio Muscinelli

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...

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

Towards Commoditizing Simulations of System Models Using Recurrent Neural Networks

Ahmet Caner Yüzügüler

System modeling and simulation plays a crucial role in the engineering of large and complex systems from various fields, such as industrial automation or power systems. In this paper, we propose a method that can be used to easily deploy high fidelity simu ...

IEEE2018