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McGill - UdeM - MPrime Machine Learning Seminars
Welcome to the official web page of the McGill-UdeM-MPrime Machine Learning Seminars. This initiative from University of Montreal and McGill University aims at - diffusing machine learning research done at both universities and stimulating discussion and collaboration
- offering introductive talks on machine learning subjects
- hosting talks by invited speakers
The seminars are held successively at University of Montreal and McGill University, and delivered in English. The time and location are specified for each seminar.
To sign up for the announcement mailing list, use our mailman interface. For questions or suggestions, contact: - Guillaume Desjardins (desjagui[at]iro[dot]umontreal[dot]ca), PhD student at UdeM (LISA lab, DIRO),
- Doina Precup (dprecup[at]cs[dot]mcgill[dot]ca), Assistant Professor at McGill,
Also, a mailing list in computational neuroscience for
researchers in Quebec has just been setup by François Rivest, student at the Département d’Informatique et de Recherche Opérationnelle. The objective of this initiative is to stimulate
research in computational neuroscience, but more importantly, to help
develop strong multi-disciplinary collaborations in that field in
Quebec.
To register on the list, go to
http://www.listes.umontreal.ca/wws/info/neuralcomp.
To publish on the
list, e-mail to neuralcomp[at]listes.umontreal.ca. Do not hesitate to
forward events of interest for computational neuroscience researchers on this list.
Next Seminar
Date: Tuesday, October 25th 2011 at 2:00 PM.
Location: LISA lab (TBD)
Speaker: Richard Socher, Stanford
Title: Recursive Deep Learning in Natural Language Processing and Computer Vision
Hierarchical and recursive structure is commonly found in different
modalities, including natural language sentences and scene images. I
will present some of our recent work on three recursive neural network
architectures that learn meaning representations for such hierarchical
structure. These models obtain state-of-the-art performance on several
language and vision tasks.
The meaning of phrases and sentences is determined by the meanings of
its words and the rules of compositionality. We introduce a recursive
neural network (RNN) for syntactic parsing which can learn vector
representations that capture both syntactic and semantic information
of phrases and sentences. For instance, the phrases "declined to
comment" and "would not disclose" have similar representations.
Since our RNN does not depend on specific assumptions for language, it
can also be used to find hierarchical structure in complex scene
images. This algorithm obtains state-of-the-art performance for
semantic scene segmentation on the Stanford Background and the MSRC
datasets and outperforms Gist descriptors for scene classification by 4%.
The ability to identify sentiments about personal experiences,
products, movies etc. is crucial to understand user generated content
in social networks, blogs or product reviews. The second architecture
I will talk about is based on recursive autoencoders (RAE). RAEs learn
vector representations for phrases sufficiently well as to outperform
other traditional supervised sentiment classification methods on
several standard datasets.
We also show that without supervision RAEs can learn features which
outperform previous approaches for paraphrase detection on the
Microsoft Research Paraphrase corpus.
Previous seminars
Here is the list of past seminars, with their corresponding slides:
Seminars 2008-2011
| [11/10/2011] |
Learning from Heterogeneous Sources via Gradient Boosting Consensus, by Jean-Francois Paiement |
[abstract] |
| [15/09/2011] |
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles, by Ludovic Arnold |
[abstract] |
| [24/08/2011] |
An overview and recent insights on Reservoir Computing, par Benjamin Schrauwen |
[abstract] |
| [23/06/2011] |
Unsupervised machine learning for analysis of EEG and MEG at rest, by Aapo Hyvarinen |
[abstract] |
| [11/05/2011] |
Wordless sounds aka privacy-sensitive audio, by Hari Parthasarathi |
[abstract] |
| [16/03/2011] |
Learning models of transformed image parts, by Brendan Frey |
[abstract] |
| [14/03/2011] |
On Exponential Families and Expressive Power of Related Generative Models, by Guido Montúfar |
[abstract] |
| [04/03/2011] |
Label Embedding Trees for Large Multi-Class Tasks, by Hugo Larochelle |
[abstract] |
| [17/12/2010] |
Label Embedding Trees for Large Multi-Class Tasks, by Samy Bengio |
[abstract] |
| [16/12/2010] |
Approximate Inference for the Loss-Calibrated Bayesian, by Simon Lacoste-Julien |
[abstract] |
| [03/12/2010] |
Adaptive stochastic search: tuning Gaussians’ covariances, by Rémi Bardenet |
[abstract] |
| [26/11/2010] |
The use of visual information during face recognition and reading, by Frédéric Gosselin |
[abstract] |
| [26/10/2010] |
Tale of a Neural Network: From Part-Of-Speech to Parsing, by Ronan Collobert |
[abstract] |
| [19/10/2010] |
Large Scale Image and Music Annotation: Learning to Rank and Multi-Tasking with Joint Embeddings
, by Jason Weston |
[abstract] |
| [07/10/2010] |
Learning Spatial and Transformational Invariants for Visual Representation, by Charles Cadieu |
[abstract] |
| [16/07/2010] |
A fast natural Newton method, by Nicolas Le Roux |
[abstract] |
| [14/04/2010] |
Modèle informatique du coapprentissage des ganglions de la base et du cortex: L’apprentissage par renforcement et le développement de représentations, by François Rivest |
[abstract] |
| [04/03/2010] |
Evaluating and Learning Invariant Features, by Ian Goodfellow |
[abstract] |
| [02/02/2010] |
Apprentissage de fonctions de classification et d’ordonnancement avec des données partiellement étiquetées
, by Massih-Reza Amini |
[abstract] |
| [24/11/2009] |
Emergence of Complex-Like Cells in a Temporal Product Network with Local Receptive Fields
, by Karol Gregor |
[abstract] |
| [20/11/2009] |
Unlocking Brain-Inspired Computer Vision: a Multi-Disciplinary, High-Throughput Approach
, by Nicolas Pinto |
[abstract] |
| [18/09/2009] |
Learning from Multiple Partially Observed Views, by Massih-Reza Amini |
[abstract] [pdf] |
| [16/07/2009] |
Probability, Pattern Recognition and Optimization Techniques for Interactive Media, by Christopher Pal |
[abstract] |
| [22/06/2009] |
Reinforcement learning and apprenticeship learning for robotic control, by Andrew Ng |
[abstract] |
| [27/03/2009] |
Machine Learning and Computational Linguistics: Tools of the Trade (or, The Data Acquisition Mental Bottleneck), by Jeremy Barnes |
[abstract] |
| [13/03/2009] |
Recent developments in learning deep networks, by Geoffrey Hinton |
[abstract] |
| [30/01/2009] |
New architectures and algorithms for solving deep-memory POMDPs, by Daan Wierstra |
[abstract] |
| [21/11/2008] |
Analysis of EEG data by means of ordinal pattern distributions, by Mathieu Sinn |
[abstract] |
| [17/11/2008] |
Cheap and Fast — But is it Good?
Evaluating Nonexpert Annotations for Machine Learning Tasks, by Rion Snow |
[abstract] |
| [11/11/2008] |
Using fast weights to improve Persistent Contrastive Divergence, by Tijmen Tieleman |
[abstract] |
| [16/10/2008] |
Visualizing high-dimensional data using t-SNE, by Geoffrey Hinton |
[abstract] |
| [10/10/2008] |
Large Scale Learning for Natural Language Processing, by Ronan Collobert |
[abstract] |
| [22/08/2008] |
Differentiable Sparse Coding, by J. Andrew Bagnell |
[abstract] |
Seminars 2007-2008
| [30/06/2008] |
Hierarchical learning and decision making by Stuart Russell |
[abstract]
|
| [26/06/2008] |
Artificial Intelligence in Seven Years? by Léon Bottou |
[abstract]
|
| [20/06/2008] |
Learning hierarchical representations of natural images by Mike Lewicki |
[abstract] [pdf]
|
| [27/05/2008] |
Learning in the Sample Compression Framework by Mohak Shah |
[abstract] [pdf]
|
| [13/05/2008] |
Deep belief networks are universal approximators and a recurrent neural network that learns to remember par Ilya Sutskever |
[abstract] [pdf] [code]
|
| [30/04/2008] |
Learning Deep Hierarchies of Sparse and Invariant Features by Yann LeCun |
[abstract]
|
| [18/04/2008] |
Restricted Boltzmann machines: Performance and behavior on image databases and future plans by Karol Gregor |
[abstract]
|
| [08/04/2008] |
Aggregate Markov Decision Processes by Hasan Mirza |
[abstract]
|
| [25/03/2008] |
Image Classification with Higher-Order Neural Models by James Bergstra |
[abstract] [pdf]
|
| [25/03/2008] |
Deep Learning with Denoising Autoencoders by Pascal Vincent |
[abstract] [pdf]
|
| [14/03/2008] |
Discriminative Methods with Structure by Simon Julien-Lacoste |
[abstract] [ppt]
|
| [04/03/2008] |
A Stochastic Algorithm for Partially Observable Markov Decision Processes (POMDPs) by François Laviolette |
[abstract]
|
| [19/02/2008] |
Modelling Image Patches With A Directed Hierarchy Of Markov Random Fields by Simon Osindero |
[abstract] [pdf]
|
| [22/01/2008] |
Open Problems in Statistical Machine Translation by Roland Kuhn |
[abstract] [ppt]
|
| [30/11/2007] |
A Bayesian approach to analyze water tank signals in the Pierre Auger experiment by Balazs Kegl |
[abstract] [pdf]
|
| [16/11/2007] |
Bayesian Reinforcement Learning by Mohammad Ghavamzadeh |
[abstract]
|
| [02/11/2007] |
Preliminary steps toward a probabilistic, decision theoretic model of dynamic scene understanding by Nando de Freitas |
[abstract]
|
| [19/10/2007] |
Learning the 2-D Topology of Images by Pascal Lamblin |
[abstract] [pdf]
|
| [28/09/2007] |
How Many Clusters? An Information-Theoretic Perspective by Susanna Still |
[abstract]
|
| [14/09/2007] |
Optimal Causal Inference by Susanna Still |
[abstract] [pdf]
|
Seminars 2006-2007
| [28/09/2006] |
Modelling high-dimensional sequential data using distributed hidden state by Geoffrey Hinton |
[abstract]
|
| [05/10/2006] |
Program Verification using Reinforcement Learning by Sami Zhioua |
[abstract]
|
| [24/10/2006] |
An Upper Bound on the Convergence Time of the Gibbs Sampler in
Ising Models by Yuichi Shiraishi |
[abstract] [ps]
|
| [31/10/2006] |
Dirichlet processes: interpretations, inference and extensions by Aaron Courville |
[abstract] [pdf]
|
| [07/11/2006] |
Bayesian Ranking using Factor Graphs and Expectation Propagation by Dumitru Erhan |
[abstract] [ppt]
|
| [14/11/2006] |
PAC-learning of Markov models with hidden state by Doina Precup |
[abstract] [pdf]
|
| [21/11/2006] |
Greedy Layer-Wise Training of Deep Networks by Yoshua Bengio |
[abstract] [ps/nips] [pdf/nips]
|
| [29/11/2006] |
Decision Making under Parameter Uncertainty by Shie Mannor |
[abstract] [pdf]
|
| [12/12/2006] |
Segment Class Models for Music by Darrell Conklin |
[abstract] |
| [14/12/2006] |
Towards Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions by Edmondo Trentin |
[abstract] [pdf]
|
| [10/01/2007] |
Modeling Appearance Patterns in Image Sets by Matt Toews |
[abstract] [pdf]
|
| [17/01/2007] |
Hierarchical Boosting and Filter Generation by Marc-Olivier LaBarre |
[abstract] [pdf]
|
| [25/01/2007] |
Wake-sleep Algorithm for Representational Learning by Hamid Reza Maei |
[abstract] [ppt]
|
| [28/02/2007] |
Machine Learning Based Robotics by Greg Grudic |
[abstract] [pdf]
|
| [14/03/2007] |
Topmoumoute Online Natural Gradient Algorithm by Nicolas Le Roux |
[abstract]
|
| [21/03/2007] |
An Anytime Error Minimization Search for Online Policy Improvement in Large POMDPs by Stephane Ross |
[abstract] [pdf]
|
| [28/03/2007] |
Real Neurons for Machine Learning by François Rivest |
[abstract] [flash]
|
| [04/04/2007] |
Sampling-based Planning with Approximate and Learned MDP Models par Cosmin Paduraru |
[abstract] [pdf]
|
| [11/04/2007] |
Majority Votes by François Laviolette |
[abstract] [pdf]
|
| [18/04/2007] |
Sequence kernels for Speaker Verification using Support Vector Machines by Jérôme Louradour |
[abstract] [pdf]
|
| [25/04/2007] |
Constituent Parsing by Classification by Joseph Turian |
[abstract]
|
| [02/05/2007] |
Optimizing Disease Outbreak Detection Methods Using Reinforcement Learning by Masoumeh Izadi |
[abstract] [ppt]
|
| [09/05/2007] |
Towards the construction of spatio-temporal data patterns in a road trafic urban network by Marc Joliveau |
[abstract] [pdf]
|
| [16/05/2007] |
Regret to the Best vs. Regret to the Average by Eyal Even-dar |
[abstract]
|
| [30/05/2007] |
Automated Hierarchy Discovery for Planning in Partially Observable Environments by Laurent Charlin |
[abstract] [pdf]
|
| [13/06/2007] |
An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation by Hugo Larochelle |
[abstract] [pdf]
|
Best Of NIPS/IJCAI 2007
- iLSTD: Eligibility Traces and Convergence Analysis [pdf], slides [pdf]
from Alborz Geramifard, Michael Bowling, Martin Zinkevich, Richard Sutton,
presented by Cosmin Paduraru
- Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning [pdf], slides [pdf]
from Ah-Hwee Tan,
presented by Marc G. Bellemare
- Efficient Failure Detection On Mobile Robots Using Particle Filters With Guassian Process Proposals
from Christian Plagemann, Dieter Fox, Wolfram Burgard,
presented by Amin Atrash
- Solving POMDPs Using Quadratically Constrained Linear Programs [pdf], slides [pdf]
from Christopher Amato, Daniel S. Bernstein, Shlomo Zilberstein,
presented by Robert Kaplow
- Bayesian Inverse Reinforcement Learning [pdf], slides [pdf]
from Deepak Ramachandran, Eyal Amir,
presented by Monica Dinculescu
- Linearly-solvable Markov decision problems [pdf], slides [pdf]
from Emanuel Todorov,
presented by Jordan Frank
- An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models [pdf], slides [pdf]
from Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle,
presented by Dumitru Erhan
- Efficient Learning of Sparse Representations with an Energy-Based Model [pdf], slides [pdf]
from MarcAurelio Ranzato, Christopher Poultney, Sumit Chopra, Yann LeCun,
presented by Pascal Lamblin
- Multi-Task Feature Learning [pdf], slides [pdf]
from Andreas Argyriou, Theos Evgeniou, Massimiliano Pontil,
presented by Olivier Delalleau
- Active learning for misspecified generalized linear models [pdf], slides [pdf]
from Francis Bach,
presented by Yoshua Bengio
- Analysis of Representations for Domain Adaptation [pdf], slides [pdf]
from Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira,
presented by Marina Sokolova
- No-regret algorithms for Online Convex Programs [pdf], slides [ppt]
from Geoffrey Gordon,
presented by Nicolas Chapados
Seminars 2005-2006
| [07/09/2005] | A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tailed Data by Julie Carreau | [abstract]
[pdf]
|
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| [14/09/2005] | A Brief Tutorial on Reinforcement Learning par Doina Precup | [abstract]
[pdf]
|
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| [21/09/2005] | The K-Shortest-Paths Approach to Approximate Dynamic Programming by Nicolas Chapados | [abstract]
[pdf]
|
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| [28/09/2005] | Active Learning in Partially Observable Markov Decision Processes by Robin Jaulmes | [abstract]
[ppt]
|
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| [05/10/2005] | Music/Voice and Genre Classification by Norman Casagrande and James Bergstra | [abstract]
[pdf]
|
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| [12/10/2005] | Musical Rhythm Similarity Measures with Symbolic Input:
Models, Algorithms and Applications by Godfried Toussaint | [abstract]
|
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| [19/10/2005] | Neural Networks in Graphical Domains by Marco Gori | [abstract]
[pdf]
[key]
|
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| [26/10/2005] | Automatic learning of adaptive treatment strategies using
kernel-based reinforcement learning by Joelle Pineau | [abstract]
[pdf]
|
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| [02/11/2005] | Echo State Networks: a Tutorial by Dan Popovici | [abstract]
[ppt]
|
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| [09/11/2005] | Dimensionality reduction for policy evaluation in MDPs by Philipp Keller | [abstract]
[pdf]
|
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| [16/11/2005] | Convolutional Neural Networks and Vision Applications by Yoshua Bengio | [abstract]
[pdf]
|
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| [16/11/2005] | RTBSS: An Online POMDP Algorithm for Complex Environments by Sébastien Pacquet | [abstract]
[pdf]
|
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| [30/11/2005] | Empirical Artificial Intelligence by Rich Sutton | [abstract]
[pdf]
|
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| [10/01/2006] | Onset Detection with Artificial Neural Networks for MIREX2005 by Alexandre Lacoste | [abstract]
[ppt]
|
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| [17/01/2006] | Predictive State Representations by Masoumeh Tabaeh Izadi | [abstract]
[ppt]
|
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| [24/01/2006] | Highlights of Hinton’s Contrastive Divergence Pre-NIPS Workshop by Yoshua Bengio and Pascal Lamblin | [abstract]
[ppt]
|
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| [07/02/2006] | La Gestion Active de Portefeuille by Marc Boucher | [abstract]
[ppt]
|
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| [28/02/2006] | Junction Tree Harmonisation by Jean-François Paiement | [abstract]
[pdf]
|
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| [20/03/2006] | Effective learning of deep belief nets, with application to handwritten digits and face images by Simon Osindero | [abstract]
|
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| [21/03/2006] | Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Learning by Yaakov Engel | [abstract]
[pdf]
|
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| [22/03/2006] | Reinforcement Learning with Gaussian Processes by Yaakov Engel | [pdf]
|
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| [24/03/2006] | Performance guarantees and stability of approximate ERM by Alexander Rakhlin | [abstract]
|
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| [27/03/2006] | Algorithmes d’inspiration géométrique, corrigés pour l’apprentissage en haute dimension by Pascal Vincent | [abstract]
|
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| [11/04/2006] | Discriminant Mixture of 3D Molecular Surface Models by Pascal Lamblin | [abstract]
[pdf]
|
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| [18/04/2006] | Continuous Neural Network by Nicolas Le Roux | [abstract]
[pdf]
|
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| [25/04/2006] | Detection and Control for the Treatment of Epilepsy by Robert D. Vincent | [abstract]
[pdf]
|
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| [28/04/2006] | Regret minimization under partial monitor by Gilles Stoltz | [abstract]
|
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| [02/05/2006] | Collaborative Filtering for Drug Discovery by Dumitru Erhan | [abstract]
[pdf]
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| [09/05/2006] | Observation Space Reduction and Active Learning for Partially
Observable Markov Decision Processes by Amin Atrash | [abstract]
|
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| [17/05/2006] | Music Plus One by Christopher Raphael | [abstract]
|
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| [18/05/2006] | Decision-making with Predictive State Representations (PSRs) by Michal James | [abstract]
|
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| [13/06/2006] | Neural Basis of Learning by François Rivest | [abstract]
[pdf]
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| [20/06/2006] | The decoder in statistical machine translation: how does it work? by Alexandre Patry | [abstract]
[pdf]
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| [27/06/2006] | Global Optimization and Evolutionary Algorithms by Javad Sadri | [abstract]
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Best-of NIPS 2006
- Temporal Abstraction in Temporal-difference Networks [pdf],
slides [pdf]
from Richard Sutton, Eddie Rafols, Anna Koop,
presented by Marc Gendron-Bellemare
- Learning Influence among Interacting Markov Chains [pdf],
slides [pdf]
from Dong Zhang, Daniel Gatica-Perez, Samy Bengio, Deb Roy,
presented by Pablo Samuel
Castro
- Robust Fisher Discriminant Analysis [pdf], slides [ppt]
from Seung-Jean Kim, Alessandro Magnani, Stephen Boyd,
presented by Erick Delage
- TD(0) Leads to Better Policies than Approximate Value Iteration [pdf], slides [pdf]
from Benjamin Van Roy,
presented by Philipp Keller - Metric Learning by Collapsing Classes [pdf],
slides [ppt]
from Amir Globerson, Sam Roweis,
presented by Dumitru Erhan
- Towards Human-level AI: Yann LeCun’s talk, slides [pdf]
from Yann LeCun,
presented by Alexandre Lacoste
- Highlights on the ’Inductive Transfer : 10 Years Later’ Workshop [html],
slides [pdf]
from Danny Silver, Goekhan Bakir, Kristin Bennett, Rich Caruana, Massimiliano Pontil, Stuart Russell, Prasad Tadepalli,
presented by Hugo Larochelle - Rodeo: Sparse Nonparametric Regression in High Dimensions [pdf],
slides [pdf]
from John Lafferty, Wasserman Larry,
presented by Olivier Delalleau - Beyond Gaussian Processes: On the Distributions of Infinite Networks [pdf],
slides [pdf]
from Ricky Der, Daniel Lee,
presented by Julie Carreau - Inferring Motor Programs From Images of Handwritten
Digits [pdf],
slides [pdf]
from Geoff Hinton, Vinod Nair,
presented by Nicolas Le Roux - Gaussian Process Dynamical Models [pdf],
slides [ppt]
from Jack Wang, Fleet David, Hertzmann Aaron,
presented by Nicolas Chapados - Divergences, surrogate loss functions and experimental design [pdf], slides [pdf]
from XuanLong Nguyen, Wainwright Martin, Jordan Michael,
presented by James Bergstra - Bayesian Sparse Sampling for On-line Reward Optimization [pdf], slides [ppt]
from Dale Schuurmans, au "Workshop on Value of Information in Inference, Learning and Decision-Making",
presented by Dan Popovici - Distance Metric Learning for Large Margin Nearest Neighbor Classification [pdf], slides [pdf]
from Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul,
presented by Gilles Godbout - Infinite latent feature models and the Indian buffet process [pdf]
from Tom Griffiths, Ghahramani Zoubin,
presented by Aaron Courville
Former Web Page
To access the former LISA seminars web page, click here
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