Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Ghosh, Arna"'
Non-contrastive self-supervised learning (NC-SSL) methods like BarlowTwins and VICReg have shown great promise for label-free representation learning in computer vision. Despite the apparent simplicity of these techniques, researchers must rely on se
Externí odkaz:
http://arxiv.org/abs/2312.10725
Autor:
Pogodin, Roman, Cornford, Jonathan, Ghosh, Arna, Gidel, Gauthier, Lajoie, Guillaume, Richards, Blake
Publikováno v:
The Twelfth International Conference on Learning Representations, 2024
A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic plasticity in the brain. However, the vast majority of this work ignores a critical underlying assumption: the
Externí odkaz:
http://arxiv.org/abs/2305.19394
To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond task accuracy, it is unclear if such learning rules converge to solutio
Externí odkaz:
http://arxiv.org/abs/2206.00823
Representation learning that leverages large-scale labelled datasets, is central to recent progress in machine learning. Access to task relevant labels at scale is often scarce or expensive, motivating the need to learn from unlabelled datasets with
Externí odkaz:
http://arxiv.org/abs/2202.05808
Autor:
Prince, Luke Y., Eyono, Roy Henha, Boven, Ellen, Ghosh, Arna, Pemberton, Joe, Scherr, Franz, Clopath, Claudia, Costa, Rui Ponte, Maass, Wolfgang, Richards, Blake A., Savin, Cristina, Wilmes, Katharina Anna
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this
Externí odkaz:
http://arxiv.org/abs/2105.05382
Neuroimaging data analysis often involves \emph{a-priori} selection of data features to study the underlying neural activity. Since this could lead to sub-optimal feature selection and thereby prevent the detection of subtle patterns in neural activi
Externí odkaz:
http://arxiv.org/abs/1805.11704
Global Average Pooling (GAP) [4] has been used previously to generate class activation for image classification tasks. The motivation behind SIMILARnet comes from the fact that the convolutional filters possess position information of the essential f
Externí odkaz:
http://arxiv.org/abs/1711.02831
Handwriting is a skill learned by humans from a very early age. The ability to develop one's own unique handwriting as well as mimic another person's handwriting is a task learned by the brain with practice. This paper deals with this very problem wh
Externí odkaz:
http://arxiv.org/abs/1611.08789
Autonomous driving is one of the most recent topics of interest which is aimed at replicating human driving behavior keeping in mind the safety issues. We approach the problem of learning synthetic driving using generative neural networks. The main i
Externí odkaz:
http://arxiv.org/abs/1611.08788