CSE 254: Seminar on Learning Algorithms

Time
TuTh 3.30-5 in CSE 2154

Instructor:
Sanjoy Dasgupta
Office hours TBA in EBU3B 4138

This quarter the theme of CSE 254 is deep learning.
Prerequisite: CSE 250AB.

The first couple of lectures will be an overview of basic material. Thereafter, in each class meeting, a student will give a talk lasting about 60 minutes presenting a technical paper (or several papers) in detail. In questions during the talk, and in the final 20 minutes, all seminar participants will discuss the paper and the issues raised by it.


Date Presenter
Paper
Slides
Jan 10 Sanjoy Introduction

 

Jan 12 Sanjoy Hopfield nets

 

Jan 17 Sanjoy Markov random fields, Gibbs sampling, simulated annealing

 

Jan 19 Sanjoy Deep belief nets as autoencoders and classifiers

 

Jan 24 Brian Task-driven dictionary learning

here

Jan 26 Vicente A quantitative theory of immediate visual recognition

here

Jan 31 Emanuele Convolutional deep belief networks

here

Feb 2 Nakul Restricted Boltzmann machines: learning, and hardness of inference

here

Feb 7 Craig The independent components of natural scenes are edge filters

here

Feb 9   No class: ITA conference at UCSD

 

Feb 14 Janani Deep learning via semi-supervised embedding

here

Feb 16 Stefanos A unified architecture for natural language processing

here

Feb 21 Hourieh An analysis of single-layer networks in unsupervised feature learning

here

Feb 23 Ozgur Emergence of simple-cell receptive properties by learning a sparse code for natural images

here

Feb 28 Matus Representation power of neural networks: Barron, Cybenko, Kolmogorov

here

Mar 1 Frederic Reinforcement learning on slow features of high-dimensional input streams

 

Mar 6 Dibyendu, Sreeparna Learning deep energy models and What is the best multistage architecture for object recognition?

here

Mar 8   No class: Sanjoy out of town

 

Mar 13 Bryan Inference of sparse combinatorial-control networks

here

Mar 15 Qiushi Weighted sums of random kitchen sinks

here

This is a four unit course in which the work consists of oral presentations.

The procedure for each student presentation is as follows:

Please read, reflect upon, and follow these presentation guidelines, courtesy of Prof Charles Elkan.  Presentations will be evaluated, in a friendly way but with high standards, using this feedback form.

The schedule of presentations will be determined as much as possible on Tuesday Jan 10.  Here is a preliminary list of papers.

If you want to change your presentation date, please arrange a swap with another student and notify me at least two weeks in advance.