Search Results for author:
Found 9 papers, 5 papers with code
Positional Encoding Helps Recurrent Neural Networks Handle a Large Vocabulary
This study discusses the effects of positional encoding on recurrent neural networks (RNNs) utilizing synthetic benchmarks.
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery
We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently governs noisy spatial-temporal observed data with few reliable terms.
Noise-aware Physics-informed Machine Learning for Robust PDE Discovery
This work is concerned with discovering the governing partial differential equation (PDE) of a physical system.
Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)
In this study, we reported our exploration of Text-To-Speech without Text (TTS without T) in the Zero Resource Speech Challenge 2020, in which participants proposed an end-to-end, unsupervised system that learned speech recognition and TTS together.
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental representations of syntactic state.
Superregular grammars do not provide additional explanatory power but allow for a compact analysis of animal song
A pervasive belief with regard to the differences between human language and animal vocal sequences (song) is that they belong to different classes of computational complexity, with animal song belonging to regular languages, whereas human language is superregular.
What do RNN Language Models Learn about Filler--Gap Dependencies?
RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they learn.
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency
Recurrent neural networks (RNNs) are the state of the art in sequence modeling for natural language.
What do RNN Language Models Learn about Filler-Gap Dependencies?
RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they learn.
