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

Yoshua Bengio yoshua.bengio at gmail.com
Fri Feb 20 12:57:12 EST 2015


Reminder. See you all at 2pm!

On Sat, Feb 14, 2015 at 2:19 PM, Kyung Hyun Cho <cho.k.hyun at gmail.com>
wrote:

> 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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>
>
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