[Lisa_teatalk] Tea Talk by Prof. Yoshua Bengio on Friday (20 Feb) at 14.00

Kyung Hyun Cho cho.k.hyun at gmail.com
Sat Feb 14 17:19:49 EST 2015


Dear all,

We will be back to the usual tea talk schedule starting from next Friday
with Yoshua introducing us a biologically plausible learning algorithm for
training a deep generative neural network. The talk will start at 14.00 in
the usual place of AA3195.

Hope to see many of you there!
- Cho

===
Speaker: Prof. Yoshua Bengio
Date/Time: 14.00 - 15.00 on Friday (20 Feb)
Place: AA3195
Title: Biologically Plausible Deep Learning
Abstract:
Neuroscientists have long criticised deep learning algorithms as
incompatible with current knowledge of neurobiology. We explore more
biologically plausible versions of deep representation learning, focusing
here mostly on unsupervised learning but developing a learning mechanism
that could account for supervised, unsupervised and reinforcement learning.
The starting point is that the basic learning rule believed to govern
synaptic weight updates (Spike-Timing Dependent Plasticity) can be
interpreted as gradient descent on some objective function so long as the
neuronal dynamics push firing rates towards better values of the objective
function (be it supervised, unsupervised, or reward-driven). The second
main idea is that this corresponds to a form of variational EM algorithm in
which the above dynamics correspond to approximate inference. Another
contribution of this paper is that the gradients required for updating the
hidden states in the above variational interpretation can be estimated
using an approximation that only requires propagating activations forward
and backward, with pairs of layers learning to form a denoising
auto-encoder. Finally, we extend the theory about the probabilistic
interpretation of autoencoders to justify improved sampling schemes based
on the generative interpretation of denoising auto-encoders, and we
validate all these ideas on generative learning tasks.
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