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Found 9 papers, 8 papers with code
Practical Kernel Tests of Conditional Independence
We describe a data-efficient, kernel-based approach to statistical testing of conditional independence.
Synaptic Weight Distributions Depend on the Geometry of Plasticity
Overall, our work shows that the current paradigm in theoretical work on synaptic plasticity that assumes Euclidean synaptic geometry may be misguided and that it should be possible to experimentally determine the true geometry of synaptic plasticity in the brain.
Efficient Conditionally Invariant Representation Learning
The procedure requires just a single ridge regression from $Y$ to kernelized features of $Z$, which can be done in advance.
Towards Biologically Plausible Convolutional Networks
This requires the network to pause occasionally for a sleep-like phase of "weight sharing".
Self-Supervised Learning with Kernel Dependence Maximization
We approach self-supervised learning of image representations from a statistical dependence perspective, proposing Self-Supervised Learning with the Hilbert-Schmidt Independence Criterion (SSL-HSIC).
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks
The state-of-the art machine learning approach to training deep neural networks, backpropagation, is implausible for real neural networks: neurons need to know their outgoing weights; training alternates between a bottom-up forward pass (computation) and a top-down backward pass (learning); and the algorithm often needs precise labels of many data points.
Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks
Reservoir computing is a powerful tool to explain how the brain learns temporal sequences, such as movements, but existing learning schemes are either biologically implausible or too inefficient to explain animal performance.
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits
Finally, we study bounds that depend on the degree of separation of the arms, generalising the results by Cowan and Katehakis [2015] from the stochastic setting to the adversarial and improving the result of Seldin and Slivkins [2014] by a factor of log(n)/log(log(n)).
Efficient Rank Minimization to Tighten Semidefinite Programming for Unconstrained Binary Quadratic Optimization
We propose a method for low-rank semidefinite programming in application to the semidefinite relaxation of unconstrained binary quadratic problems.
Optimization and Control
