Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...
Despite a growing number of studies, the neurophysiology of adult vocabulary acquisition is still poorly understood. One reason is that paradigms that can easily be combined with neuroscientfic methods are rare. Here, we tested the efficiency of two paradi ...
We study the dynamics of a network consisting of N diffusively coupled, stable- limit-cycle oscillators on which individual frequencies are parametrized by wk,k=1,...,N. We introduce a learning rule which influences the wk by driving the system t ...
Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extract the most slowly varying features from a quickly varying signal. It has been successfully applied to the unsupervised learning of translation-, rotation-, ...
This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that ...
Perceptual learning has received enhanced interest during the last years both from theoreticians and experimentalists. Recent experimental results reveal that mechanisms underlying perceptual learning are more complex than previously expected, thereby ruli ...
We study the task of learning to rank images given a text query, a problem that is complicated by the issue of multiple senses. That is, the senses of interest are typically the visually distinct concepts that a user wishes to retrieve. In this paper, we p ...
Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in reg ...
Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types o ...
All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examples are the use of non-ideal activation functions which are truncated, asymmetric, and have a non-standard gain, restriction of the network parameters to no ...
