International Conference on Learning Representations (ICLR) 2016, San Juan

International Conference on Learning Representations (ICLR) 2016, San Juan

21 Videos · May 2, 2016

About

ICLR is an annual conference sponsored by the Computational and Biological Learning Society.

It is well understood that the performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field, and include in it topics such as deep learning and feature learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.

Despite the importance of representation learning to machine learning and to application areas such as vision, speech, audio and NLP, there was no venue for researchers who share a common interest in this topic. The goal of ICLR has been to help fill this void.

Videos

Opening Remarks

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06:43

Opening

May 27, 2016

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4188 views

Keynote Talks

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34:34

Should Model Architecture Reflect Linguistic Structure?

Chris Dyer

May 27, 2016

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7957 views

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35:37

Deep Robotic Learning

Sergey Levine

May 27, 2016

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12880 views

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39:39

Guaranteed Non-convex Learning Algorithms through Tensor Factorization

Animashree Anandkumar

May 27, 2016

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4811 views

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39:17

Incorporating Structure in Deep Learning

Raquel Urtasun

May 27, 2016

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13551 views

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39:29

Beyond Backpropagation: Uncertainty Propagation

Neil D. Lawrence

May 27, 2016

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5669 views

Best Paper Awards

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17:19

Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantiz...

Song Han

May 27, 2016

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20571 views

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15:35

Neural Programmer-Interpreters

Scott Reed

May 27, 2016

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5935 views

Lectures

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17:29

Towards Universal Paraphrastic Sentence Embeddings

John Wieting

May 27, 2016

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2364 views

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15:16

The Variational Fair Autoencoder

Christos Louizos

May 27, 2016

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2639 views

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16:19

Variational Gaussian Process

Dustin Tran

May 27, 2016

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3180 views

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19:18

Convergent Learning: Do different neural networks learn the same representations...

Jason Yosinski

May 27, 2016

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10148 views

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17:11

BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large V...

Shihao Ji

May 27, 2016

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2127 views

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15:52

Net2Net: Accelerating Learning via Knowledge Transfer

Tianqi Chen

May 27, 2016

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4124 views

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12:33

Generating Images from Captions with Attention

Elman Mansimov

May 27, 2016

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2598 views

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15:55

Order-Embeddings of Images and Language

Ivan Vendrov

May 27, 2016

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4048 views

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16:47

A note on the evaluation of generative models

Lucas Theis

May 27, 2016

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2764 views

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16:08

The Goldilocks Principle: Reading Children's Books with Explicit Memory Represen...

Felix Hill

May 27, 2016

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2589 views

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16:19

Regularizing RNNs by Stabilizing Activations

David Scott Krueger

May 27, 2016

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2841 views

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18:34

Density Modeling of Images using a Generalized Normalization Transformation

Johannes Ballé

Jun 15, 2016

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4218 views

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10:35

Neural Networks with Few Multiplications

Zhouhan Lin

May 27, 2016

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2345 views