Learning Paraphrastic Representations of Natural Language Sentences

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,800
Published on ● Video Link: https://www.youtube.com/watch?v=fC0j6mEFdZE


Duration: 39:44
632 views
9

Kevin Gimpel, TTI Chicago
Representation Learning
https://simons.berkeley.edu/talks/kevin-gimpel-2017-3-31



Other Videos By Simons Institute for the Theory of Computing


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
2017-03-29Tractable Learning in Structured Probability Spaces
2017-03-29Spotlight Talk: Convolutional Dictionary Learning through Tensor Factorization
2017-03-29Representation Learning for Reading Comprehension
2017-03-29Evaluating Neural Network Representations Against Human Cognition


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Kevin Gimpel
Representation Learning