Xiang Gao

Review of Deep Neural Network Based on Auto-encoder

2019 · DEStech Transactions on Computer Science and Engineering

Deep Learning gets a new research direction of machine learning. After years of deep learning development, researchers have put forward several types of neural network built on the Auto-encoder. In this article, firstly, the origins …

MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform

2020 · arXiv (Cornell University)

We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation. We unify existing text generation algorithms in a shared codebase and further adapt earlier algorithms for constrained …

Dialogue Response Ranking Training with Large-Scale Human Feedback Data

2020

Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conversational models are increasingly capable of …

LASSO-based high-frequency return predictors for profitable Bitcoin investment

2021 · Applied Economics Letters

This article explores the Bitcoin return predictability of variables constructed from one-minute high-frequency Bitcoin trading data. During the training period of 2012–2018, LASSO is used to pick out the most powerful predictors. We then use …

Patching Weak Convolutional Neural Network Models through Modularization and Composition

2022

Despite great success in many applications, deep neural networks are not always robust in practice. For instance, a convolutional neuron network (CNN) model for classification tasks often performs unsatisfactorily in classifying some particular classes of …

Modularizing while Training: A New Paradigm for Modularizing DNN Models

2023 · arXiv (Cornell University)

Deep neural network (DNN) models have become increasingly crucial components in intelligent software systems. However, training a DNN model is typically expensive in terms of both time and money. To address this issue, researchers have …

Jointly Optimizing Diversity and Relevance in Neural Response Generation

2019

Xiang Gao, Sungjin Lee, Yizhe Zhang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 …

DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

2020

Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2020.

A Controllable Model of Grounded Response Generation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, …

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

2019 · arXiv (Cornell University)

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends …

Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space

2020

When trained effectively, the Variational Autoencoder (VAE) In this paper, we propose the first large-scale language VAE model OPTIMUS 1 . A universal latent embedding space for sentences is first pre-trained on large text corpus, …