Hui Jiang

State-Clustering Based Multiple Deep Neural Networks Modeling Approach for Speech Recognition

2015 · IEEE/ACM Transactions on Audio Speech and Language Processing

The hybrid deep neural network (DNN) and hidden Markov model (HMM) has recently achieved dramatic performance gains in automatic speech recognition (ASR). The DNN-based acoustic model is very powerful but its learning process is extremely …

Simplified Hierarchical Recurrent Encoder-Decoder for Building End-To-End Dialogue Systems

2018 · arXiv (Cornell University)

As a generative model for building end-to-end dialogue systems, Hierarchical Recurrent Encoder-Decoder (HRED) consists of three layers of Gated Recurrent Unit (GRU), which from bottom to top are separately used as the word-level encoder, the …

A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases

2019 · arXiv (Cornell University)

Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One popular way to solve the KB-QA problem is to make use of a pipeline of several NLP modules, including …

Higher Order Recurrent Neural Networks

2016 · arXiv (Cornell University)

In this paper, we study novel neural network structures to better model long term dependency in sequential data. We propose to use more memory units to keep track of more preceding states in recurrent neural …

Kullback-Leibler-Based Discrete Failure Time Models for Integration of Published Prediction Models with New Time-To-Event Dataset

2021 · arXiv (Cornell University)

Prediction of time-to-event data often suffers from rare event rates, small sample sizes, high dimensionality and low signal-to-noise ratios. Incorporating published prediction models from large-scale studies is expected to improve the performance of prognosis prediction …

Research on Fault Prediction and Diagnosis of Mechanical and Electrical Equipment in Construction Machinery based on Big Data Technology

2024 · Academic Journal of Science and Technology

This article aims to explore how to use big data technology for fault prediction and diagnosis of mechanical and electrical equipment in construction machinery. Through steps such as data collection and storage, data feature extraction …

Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference.

2016 · arXiv (Cornell University)

Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is notoriously challenging but is fundamental to natural language understanding and many applications. With the availability of large annotated data, …

Distraction-based neural networks for modeling documents

2016 · International Joint Conference on Artificial Intelligence

Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended …

Enhanced LSTM for Natural Language Inference

2017

Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data In this paper, we present a new state-of-the-art result, achieving …