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

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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.