Dear all,
I have some last minute changes to the schedule of tea talks this week:
(1) Colin Devin's Tea Talk on Wednesday at 13.00 (AA3195) (2) Yoshua Bengio's Tea Talk on Friday at 13.00 (AA3195)
The first one is the already announced one, and there is no change. The second one has been scheduled this morning, and Yoshua will tell us about his new idea on a new learning criterion for deep neural networks.
I'm attaching the abstract by Yoshua at the end of this email.
Best, - Cho
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Speaker: Prof. Yoshua Bengio (University of Montreal) Title: How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation Abstract: We discuss the usefulness and rationality of optimism in general sequential decision making. Humans are often optimistically biased and optimists are achieving more of their ambitions than more rational people. We provide a mathematical analysis of general optimistic agents and identify how they can be better or worse than strictly rational agents. These agents select the most optimistic among the still plausible hypotheses from a class. Further, we discuss a milder form of optimism in the case of continuously parameterized classes. We refer to this setting as reward-modulated inference, a framework that includes recent models for synaptic weight updates in neuroscience as a special case. At the center is a trade-off between assigning high probability to likable or likely events. A nice consequence is that we can formulate generative and discriminative learning as endpoints of a continuum. Finally, I present some first experiments using autoencoders for the classical MNIST task showing a very simple way of getting ok but not state-of-the art accuracy. The aim is not to optimize narrow tasks maximally but to be able to achieve the law of effect, making choices with a frequency proportional to past rewards in situations deemed similar. In really complex tasks this can be a sufficient objective and one that humans and animals often settle for. Also, the strategy provides suitable exploration as well as robustness to change.