Word Representation Learning without unk Assumptions

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=-QWYaPAltNs


Duration: 47:18
1,169 views
23

Chris Dyer, Carnegie Mellon University
Representation Learning
https://simons.berkeley.edu/talks/chris-dyer-2017-3-31



Other Videos By Simons Institute for the Theory of Computing


2017-04-11Additive Structure of Sets of Fourier Coefficients
2017-04-11Vinogradov's Three Primes Theorem with Primes from Special Sets
2017-04-11The Entropy Decrement Method and the Erdos Discrepancy Problem
2017-04-10Sparse Dense Dichotomy and Liquid Graphs
2017-04-10Trading Information Complexity for Error
2017-04-10Large Deviations for Arithmetic Progressions
2017-04-10Sum of Squares Lower Bounds for Refuting Any CSP
2017-04-10Algorithmic Dense Model Theorems and Weak Regularity
2017-04-04In Pursuit of Structure: Why a Little Randomness (Almost) Always Helps
2017-03-31Spotlght Talk: Performance Guarantees for Transferring Representations
2017-03-31Word Representation Learning without unk Assumptions
2017-03-31Resilient Representation and Provable Generalization
2017-03-31Learning Representations for Active Vision
2017-03-31Formation and Association of Symbolic Memories in the Brain
2017-03-31Learning Paraphrastic Representations of Natural Language Sentences
2017-03-30Provably Learning of Noisy-or Networks
2017-03-30Unsupervised Representation Learning
2017-03-30Generalization and Equilibrium in Generative Adversarial Nets (GANs)
2017-03-30Representation Learning of Grounded Language and Knowledge: with and without End-to-End Learning
2017-03-30Re-Thinking Representational Learning in Robotics and Music
2017-03-29Spotlight Talk: How to Escape Saddle Points Efficiently


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Chris Dyer
Representation Learning