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Probabilistic Inference Group (PIGS)

The Probabilistic Inference Group (PIGS) is a paper discussion group with meetings held fortnightly. The group focuses on probabilistic and information theoretic approaches to machine learning problems. Meetings are generally held fortnightly on Mondays at 10:30am in room 2.33 of the Informatics Forum. Announcements are made through the PIGS mailing list.

Instructions for presenters:

1) Choose a mainstream ML paper (or two).
2) Provide paper(s) at least one week in advance of the meeting.
3) Lead a discussion of the paper(s) in the meeting.

Mainstream means a paper that does not depend heavily on domain specific background to be comprehensible. A possible test would be to consider if the techniques could fairly readily be transferred to another application area. Papers from conferences like NIPS, ICML, UAI, AISTATS, and journals like JMLR and ML papers from IEEE PAMI are likely to be in scope; but note that papers from other sources could well fit too.

Students should discuss their paper selections with their supervisor to make sure they are reasonable choices. It is acceptable to relate the selected papers to the presenter's research, but not at the expense of discussion of the selected paper.

If people want to make thematic groupings of readings it should be possible to arrange swaps in the rota in order to make this happen.

Academic Year 2014-2015

Upcoming discussions:

Apr 6: Sohan

Apr 20: Matt

May 18: Amos

Jun 1: Partha

Jun 15: Pol

Jun 29: George

Past discussions:

Mar 23: Jinli

Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin et al.

Mar 9: Zhanxing

Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding

Feb 23: Konstantinos

Modeling Deep Temporal Dependencies with Recurrent Grammar Cells

Vincent Michalski, Roland Memisevic, Kishore Konda

Feb 9: Iain

Stochastic Variational Inference

Matthew D. Hoffman, David M. Blei, Chong Wang, John Paisley

Jan 26: NIPS 2014 review

Krzysztof: Do Deep Nets Really Need to be Deep? Jimmy Ba, Rich Caruana

Amos: Generative Adversarial Nets Ian J. Goodfellow et al.

Gavin: Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing Zhang, Yuchen, et al.

Pol: Learning Generative Models with Visual Attention Yichuan Tang et al.

Jinli: Factoring Variations in Natural Images with Deep Gaussian Mixture Models Aaron van den Oord, Benjamin Schrauwen

Chris: Sequence to sequence learning with neural networks Sutskever, Vinyals, Le

Yichuan: A Multiplicative Model for Learning Distributed Text-Based Attribute Representations Kiros, et al.

Jari: Unsupervised Transcription of Piano Music Berg-Kirkpatrick et al.

Jan 12: Dr Mike Smith (visitor talk, Makerere University), Title: Informatics in a Developing Country (Pollution, Traffic and Malaria)

Meetings in 2014

Meetings in 2013

Meetings in 2012

Meetings in 2011

Meetings in 2010

Meetings in 2009

Meetings in 2008

Meetings in 2007

Earlier meetings (2002-2006) on old website

Topic attachments
I Attachment Action Size Date Who Comment
zipzip manage 4178.0 K 15 Jul 2008 - 12:53 Main.s0565918  
pdfpdf latent-models-covariance.pdf manage 276.1 K 20 Jul 2007 - 13:38 Main.s9810791 Latent models for cross-covariance (PIGS 24th July 2007)
Topic revision: r389 - 23 Mar 2015 - 12:00:32 - Main.s1152840
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