Danny TarlowPh.D. StudentMachine Learning Research Group Department of Computer Science University of Toronto dtarlow cs toronto edu |
|
| • | I started my Postdoc at Microsoft Research Cambridge. I'll keep this website up to date for now. |
| • | I am co-organizing a workshop on "Perturbations, Optimization, and Statistics" at NIPS 2012. The Call for Papers is now up. |
| • | In January 2013, I will be joining Microsoft Research Cambridge as a Postdoctoral Researcher and will be a Research Fellow at Darwin College at the University of Cambridge. |
Learning Articulated Skeletons From Motion (2007)
David Ross,
Daniel Tarlow, and
Richard Zemel.
Workshop on Dynamical Vision at International Conference on Computer Vision (WDV-ICCV 2007).
[pdf]
[webpage]
[bibtex]
Using Combinatorial Optimization within Max-Product Belief Propagation (2006)
John Duchi,
Daniel Tarlow,
Gal Elidan, and
Daphne Koller.
Advances in Neural Information Processing Systems (NIPS 2006).
Spotlight Presentation
[pdf]
[bibtex]
Adaptive Tree CPDs in Max-Product Belief Propagation (2006)
Daniel Tarlow.
Course project for CSC2515, taught by Sam Roweis.
[pdf]
Efficient Machine Learning with Combinatorial and High Order Structures
Microsoft Research Cambridge, Cambridge, UK, Spring 2012.
Graph Cuts is a Max-Product Algorithm
Uncertainty in Artificial Intelligence, Barcelona, Spain. Summer 2011.
Dynamic Tree Block Coordinate Ascent
International Conference on Machine Learning, Bellevue, Washington. Summer 2011.
[pptx] [pdf]
Learning with High Order Models and Loss Functions
Toronto Machine Learning Group Seminar, Toronto, Canada, Fall 2010.
Robust and Efficient Schedules for Chain and Grid Models in Infer.NET
Microsoft Research Cambridge, Cambridge, UK, Summer 2010.
On the Relationship Between Graph Cuts and Max-Product Belief Propagation
CBL Lab, University of Cambridge, Cambridge, UK, Summer 2010.
Max-Product Belief Propagation in High Order Factor Graphs
Toronto Machine Learning Group Seminar, Toronto, Canada, Fall 2009.
Microsoft Research Cambridge, Cambridge, UK, Summer 2009.
Automatically Calibrating a Probabilistic Graphical Model of Building Energy Consumption
IBPSA Conference on Building Simulation, Glasgow, Scotland, Summer 2009.
Flexible Priors for Exemplar-based Clustering
Uncertainty in Artificial Intelligence, Helsinki, Finland,
Summer 2008.
Toronto Machine Learning Group Seminar,
Toronto, Canada, Winter 2008.
[pdf]
[video]
Learning Articulated Skeletons from Motion
CIFAR Summer School on Neural Computation and Adaptive Perception,
Summer 2007.
[pdf]
Using Combinatorial Optimization within Max-Product Belief Propagation
Toronto Machine Learning Group Seminar, Toronto, Canada,
Fall 2006.
[ppt] [pdf]
The Role of Features in a Feedback-based Ranking System
Google TechTalk,
Mountain View, California, Summer 2006.
Partition-based Inference in Markov Networks
with John Duchi.
Daphne Koller group,
Stanford, California, Spring 2006.
Learning in General Games
with Lee Zen and Ankit Garg.
Stanford Logic Group,
Stanford, California, Winter 2005.
[link]
Automated Grading of Logic-based Homework Problems
Stanford Logic Group,
Stanford, California, Summer 2004.