Search Results for author:
Found 9 papers, 2 papers with code
Towards Learning Universal Hyperparameter Optimizers with Transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution.
Learning Robust and Multilingual Speech Representations
Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance.
Unsupervised Learning of Efficient and Robust Speech Representations
We present an unsupervised method for learning speech representations based on a bidirectional contrastive predictive coding that implicitly discovers phonetic structure from large-scale corpora of unlabelled raw audio signals.
Learning to Discover, Ground and Use Words with Segmental Neural Language Models
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 character sequences.
Unsupervised Word Discovery with Segmental Neural Language Models
We propose a segmental neural language model that combines the representational power of neural networks and the structure learning mechanism of Bayesian nonparametrics, and show that it learns to discover semantically meaningful units (e. g., morphemes and words) from unsegmented character sequences.
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
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.
Neural Architectures for Named Entity Recognition
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 training corpora that are available.
Ranked #8 on
Named Entity Recognition (NER)
on CoNLL++
Learning to Represent Words in Context with Multilingual Supervision
We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts.
