15 citations · 76 across the 18 of their papers we have counts for
30 papers
Synaptic Weight Distributions Depend on the Geometry of Plasticity
Roman Pogodin, Jonathan Cornford, Arna Ghosh +3
A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic plasticity in the brain. However, the va…
Steerable Equivariant Representation Learning
Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang +4
Pre-trained deep image representations are useful for post-training tasks such as classification through transfer learning, image retrieval, and object detection. Data augmentation…
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie
Many learning algorithms used as normative models in neuroscience or as candidate approaches for learning on neuromorphic chips learn by contrasting one set of network states with…
Sources of Richness and Ineffability for Phenomenally Conscious States
Xu Ji, Eric Elmoznino, George Deane +5
Conscious states (states that there is something it is like to be in) seem both rich or full of detail, and ineffable or hard to fully describe or recall. The problem of ineffabili…
Reliability of CKA as a Similarity Measure in Deep Learning
MohammadReza Davari, Stefan Horoi, Amine Natik +3
Comparing learned neural representations in neural networks is a challenging but important problem, which has been approached in different ways. The Centered Kernel Alignment (CKA)…
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer
Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson +2
When presented with a data stream of two statistically dependent variables, predicting the future of one of the variables (the target stream) can benefit from information about bot…