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Publications associées (63)
Unraveling brain interactions in vision: the example of crowding
Michael Herzog, Bogdan Draganski, Vitaly Chicherov, Maya Anna Jastrzebowska
In visual crowding, the presence of neighboring elements impedes the perception of a target. Crowding is traditionally explained with feedforward, local models. However, increasing the number of neighboring elements can decrease crowding, i.e., lead to unc ...
2021Reservoir Computing meets Recurrent Kernels and Structured Transforms
Florent Gérard Krzakala, Jonathan Yuelin Dong, Ruben Elie Ohana
Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained. In the large size limit, such random neural networks have a deep connection with kernel m ...
Curran Associates, Inc.2020Crowding and the Architecture of the Visual System
Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...
EPFL2020Capsule networks as recurrent models of grouping and segmentation
Michael Herzog, Adrien Christophe Doerig, Bilge Sayim, Mauro Manassi, Lynn Schmittwilken
Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we pre ...
2020Multilingual Training and Adaptation in Speech Recognition
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