27 citations · 80 across the 7 of their papers we have counts for
7 papers
Towards Learning Universal Hyperparameter Optimizers with Transformers
Yutian Chen, Xingyou Song, Chansoo Lee +9
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar…
Learning Robust and Multilingual Speech Representations
Kazuya Kawakami, Luyu Wang, Chris Dyer +2
Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognitio…
Learning to Discover, Ground and Use Words with Segmental Neural Language Models
Kazuya Kawakami, Chris Dyer, Phil Blunsom
We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented c…
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
Kazuya Kawakami, Chris Dyer, Phil Blunsom
Fixed-vocabulary language models fail to account for one of the most characteristic statistical facts of natural language: the frequent creation and reuse of new word types. Althou…
Character Sequence Models for ColorfulWords
Kazuya Kawakami, Chris Dyer, Bryan R. Routledge +1
We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale color--name pairs obtained from an…
Neural Architectures for Named Entity Recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian +2
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised train…
Learning to Represent Words in Context with Multilingual Supervision
Kazuya Kawakami, Chris Dyer
We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations a…