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NIPS Online

Invited Speakers at Previous Editions of the Learning@Snowbird Workshop


Deep Learning of Hierarchical Structure
Chris Manning (Stanford)
Go With The Flow: A New Manifold Modeling and Learning Framework for Image Ensembles
Richard Baraniuk (Rice)
Convolution Networks with Stable Invariants
Stéphane Mallat
Learning Matrix Decomposition Structures
Bill Freeman (MIT)
Decision-Theoretic Models of Perception and Action
Lawrence Maloney (NYU)


Collaborative, Structured, and Hierarchical Sparse Image Models
Guillermo Sapiro (U. Minnesota)
Can computer vision help us understand biological vision?
Mitya Chkolvskii (HHMI Janelia Farm)
Inverse Reinforcement Learning for Following Instructions
Nicholas Roy and Thomas Kollar and Stefanie Tellex (MIT)
Are categories necessary for recognition?
Alyosha Efros (CMU)
1. Human versus machine: comparing visual object recognition systems on a level playing field
Nicolas Pinto (MIT) and David D. Cox
Markov Logic Networks: A Step Towards a Unified Theory of Learning and Cognition
Pedro Domingos (U. Washington)
Object Detection Grammars
David McAllester (U. Chicago, TTI-C)
An Iterative Path Integral Reinforcement Learning Approach
Evangelos Theodorou (USC)


Terence Sanger (Stanford)
Failure of Motor Learning and How to Succeed Anyway
Max Welling (UC Irvine)
On Herding Dynamical Weights and Fractal Geometry
Russ Tedrake (MIT)
Learning to fly like a bird


Nathan Intrator (Tel-Aviv)
Novel Interpretation for EEG Data and Prediction of Epileptic Seizure
Ronan Collobert and Jason Weston (NEC Labs)
Natural Language Processing: The Brain Way
Dan Klein (UC Berkeley)
Denis Pelli (NYU)
The uncrowded window for object recognition
Rob Fergus (NYU)
Large image databases and small codes for object recognition
Alon Orlitsky (UCSD)
Andrew Y. Ng (stanford)
STAIR: The STanford Artificial Intelligence Robot project
Demetri Terzopoulos
Learning Neuromuscular Control for the Biomechanical Simulation of the Neck-Head-Face Complex


Andrew Fitzgibbon (Microsoft Research, Cambridge)
Matrix factorization with missing data
Brendan Frey (University of Toronto)
Explorations into the cellular transcriptome
Don Geman (Johns Hopkins University)
Small-Sample Learning in Computational Biology
Ralf Herbrich (Microsoft Research, Cambridge)
Bayesian ranking
Larry Jackel (DARPA IPTO/TTO)
What Are the DARPA Ground Robots Learning?
David Stork (Ricoh)
Did the great masters "cheat" using optics? Computer image analysis of Renaissance masterpieces sheds light on a controversial theory


Daniel Huttenlocher (Cornell)
Graphical Models for Object Recognition
Larry Matthies (Jet Propulsion Laboratory)
Perception for Autonomous Navigation Off-road and Off-planet
Michael Kearns (University of Pennsylvania)
Game Theory, Economics, and the Effects of Network Structure
Denis Pelli (New York University)
Human vision isolates parts to recognize the whole


Piotr Indyk (MIT)
Algorithmic Applications of Low-Distortion Embeddings
Andrew Zisserman
Object class recognition using trainable visual models
Shimon Ullman (Weizmann Institute)
Visual object classification, recognition and segmentation
Jean-Philippe Vert (Ecole des Mines de Paris)
Kernel Methods in Computational Biology
Lior Pachter (UC Berkeley)
Parametric Inference for Biological Sequence Analysis
Eero Simoncelli (NYU)
Learning the Statistical Structure of the Visual World


Adam Arkin (Lawrence Berkeley Lab)
Graphical Model Based Yeast Haploinsufficiency Profiling for Determining Drug Mechanisms and Interactions
Manuela Veloso (CMU)
Dynamic Multi-Robot Coordination in the Presence of Teammates and Adversaries
Martial Hebert (CMU)
Approaches to Object Recognition in Images


Eric Brill (Microsoft)
Automatic Question-Answering - Letting the Data do All the Work
James M. Robins (Harvard School of Public Health)
Estimation of Optimal Treatment Strategies
Richard Durbin (Sanger Institute)
Finding genes in the human genome sequence
Moshe Tennenholtz (Technion)
Efficient reinforcement learning in hostile environments
Dan Ellis (Columbia University)
Auditory Scene Analysis: Computational models of sound organization
Oren Etzioni (University of Washington)
Crossing the Chasm in Machine Learning: moving from function approximation to complete learning systems
Thomas Vetter (University of Freiburg)
Learning the appearance of faces: A Morphable Face Model for the analysis and the synthesis of images.
Dan Koditschek (University of Michigan)
Better Work: Saying What to Learn and Learning What to Say to Your Legs


M. Hasselmo (Boston University)
Modeling the role of the hippocampus in goal-directed spatial navigation.
Martin A. Nowak (Institute for Advanced Study, Princeton)
Evolution of language
Jon Kleinberg
Decentralized Network Algorithms: Small­World Phenomena and the Dynamics of Information
Daniel M. Wolpert (University College, London)
Computational human sensorimotor control
Hugh Durrant-Whyte, Eduardo Nebot, Gamini Dissanayake (U. Sydney)
Autonomous Localisation and Map building in Large-Scale Unstructured Environments
Gary Blasdel, Niall P. McLaughlin (Harvard)
Interactions Between Retinotopy, Ocular Dominance, and Orientation in Monkey V1
Jean Ponce (U. Illinois)
Issues in 3D object recognition


Craig Boutillier (University of British Columbia)
Factored POMDPs and Belief State Approximation
Geoffrey Hinton (Gatsby Unit, University College London)
Training Products of Experts by Maximizing Contrastive Likelihood
John Lafferty (Carnegie Mellon University )
Learning Language Models for Information Retrieval
Shun-Ichi Amari (U. of Tokyo - Riken)
Information Geometry of Multilayer Perceptrons
Trevor Hastie (Stanford University )
Gene Shaving: a new class of clustering methods for expression arrays
Ehud Kalai (Northwestern University)
Rational Interactive Learning
Deborah Gordon (Stanford)
The Organization of Work in Ant Colonies


Tom M. Mitchell (Carnegie Mellon University)
"Supervised Learning from Unlabeled Data"
Tomaso Poggio (MIT)
"A trainable system for visual object detection and classification"
Pietro Perona (Caltech)
"Where is the Sun: An Exploration of Pre-Shape Perception"
Alex Pentland (MIT Media Laboratory)
"Learning Human Behaviors"
Alistair Sinclair (UC Berkeley)
"Convergence Rates for Monte Carol Experiments"
Eric Winfree (Princeton and Caltech)
"Architect Collective Computation by Molecules"
David Haussler (University of California) Tommi Jaakkola (MIT), Mark Diekhans,
"Hidden markov models and Fisher kernels for biosequence analysis"
Lorin Grubb (Carnegie Mellon University)
"A Statistical Method for Tracking Live Vocal Performances"

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