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ABC
AdaptiveDA
ALEPH
ApproxBayesInf
BayesNonparam
BayesOpt
BigNeuro
BlackBox
CiML
CognitiveComputation1
CognitiveComputation2
ComplexNetworks
DeepRL
Extreme
FasterFromEasy
FeatureEx
Metareasoning
MLCB
MLEcom
MLHC
MLSys
MultiAgent
Multimodal
Multiresolution
NetworksInScience
Neuroimaging1
Neuroimaging2
NonConvexOpt
NonParam
OPT
Personalization
Privacy
ProbIntegration
QuantumML
RAM
ScalableMC
SpokenLanguage
StatsNeuralSys
TimeSeries
TransferAndMultitask
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#1
Yann Lecun, Unsupervised Learning (TBA)
#2
Yoshua Bengio
#3
Yoshua Bengio, Small Steps Towards Biologically Plausible Deep Learning
#4
Panel: Deep Learning and neuroscience: What can brains tell us about massive computing and vice versa?
#5
Juergen Schmidhuber
#6
Neil Lawrence, The Mechanistic Fallacy and Modelling how we Think
#7
Sergey Levine
#8
Deep Learning RNNaissance. Jurgen Schmidhuber
#9
Yixuan Li, Convergent Learning: Do different neural networks learn similar features?
#10
Real-time Predictions using Time-series Data, Devavrat Shah (MIT)
#11
Panel discussion: Modern Challenges in Time Series Analysis
#12
Josh Tenenbaum
#13
Vlad Mnih
#14
Emily Fox, University of Washington (invited)
#15
Martin Riedmiller
#16
Dmitry Storcheus, A Survey of Modern Questions and Challenges in Feature Extraction
#17
Why would you recommend me THAT!?, Aish Fenton (Netflix)
#18
Gerry Tesauro
#19
Pulkit Agrawal The Human Visual Hierarchy is Isomorphic to the Hierarchy learned by a Deep Convolutional Neural Network Trained for Object Recognition
#20
Emily Fox: Variational inference for large-scale and streaming sequential data
#21
Jan Koutnik
#22
Honglak Lee
#23
Michael Bowling
#24
Osaro presentation
#25
Antoine Bordes
#26
Ruslan Salakhutdinov (CMU), Generating Images from Captions with Attention
#27
Panel: On the Foundations and Future of Approximate Inference
#28
How to learn an algorithm, Juergen Schmidhuber (Invited)
#29
A Roadmap towards Machine Intelligence, Tomas Mikolov, Facebook AI Research (Invited)
#30
From Attention to Memory and towards Longer-Term Dependencies, Yoshua Bengio (Invited)
#31
Alex Graves, Google Deepmind (Invited)
#32
Learning to learn neural networks, Tom Bosc, Inria.
#33
Invited Talk: Jeff Dean
#34
Yann LeCun
#35
Exploiting cognitive constraints to improve machine-learning memory models, Mike Mozer (Invited)
#36
The Neural GPU and the Neural RAM machine, Ilya Sutskever, Google (Invited)
#37
Structured Memory for Neural Turing Machinesî Wei Zhang, Yang Yu, Bowen Zhou (IBM Watson) (Contributed)
#38
Evolving Neural Turing Machines, Rasmus Boll Greve
#39
Neural Machine Translation: Progress Report and Beyond, Kyunghyun Cho, NYU (Invited)
#40
Sleep, learning and memory: optimal inference in the prefrontal cortex, Adrien Peyrache, NYU (Invited)
#41
Yoshua Bengio
#42
Towards Neural Network-based Reasoning, Baolin Peng, (Contributed)
#43
Neural Models for Simple Algorithmic Games, Sainbayar Sukhbaatar (Contributed)
#44
Seth Lloyd (Intro to quantum machine learning)
#45
Generating Images from Captions with Attention, Elman Mansimov (Contributed)
#46
Josh Tenenbaum
#47
John Langford, Microsoft Research: 'An Interactive Learning Platform for Making Decisions'
#48
Zoubin Ghahramani
#49
Scaling and generalizing variational inference by David Blei (Columbia University)
#50
Ruslan Salakhutdinov
Popular by Type
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ABC
AdaptiveDA
ALEPH
ApproxBayesInf
BayesNonparam
BayesOpt
BigNeuro
BlackBox
CiML
CognitiveComputation1
CognitiveComputation2
ComplexNetworks
DeepRL
Extreme
FasterFromEasy
FeatureEx
Metareasoning
MLCB
MLEcom
MLHC
MLSys
MultiAgent
Multimodal
Multiresolution
NetworksInScience
Neuroimaging1
Neuroimaging2
NonConvexOpt
NonParam
OPT
Personalization
Privacy
ProbIntegration
QuantumML
RAM
ScalableMC
SpokenLanguage
StatsNeuralSys
TimeSeries
TransferAndMultitask
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