Search Results for author: Ke Hu

Found 16 papers, 2 papers with code

Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling

no code implementations5 May 2022 Xin Chen, Qingtao Tang, Ke Hu, Yue Xu, Shihang Qiu, Jia Cheng, Jun Lei

In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction.

Click-Through Rate Prediction

Streaming Align-Refine for Non-autoregressive Deliberation

no code implementations15 Apr 2022 Weiran Wang, Ke Hu, Tara N. Sainath

We propose a streaming non-autoregressive (non-AR) decoding algorithm to deliberate the hypothesis alignment of a streaming RNN-T model.

Continual Learning for CTR Prediction: A Hybrid Approach

no code implementations18 Jan 2022 Ke Hu, Yi Qi, Jianqiang Huang, Jia Cheng, Jun Lei

To address this problem, we formulate CTR prediction as a continual learning task and propose COLF, a hybrid COntinual Learning Framework for CTR prediction, which has a memory-based modular architecture that is designed to adapt, learn and give predictions continuously when faced with non-stationary drifting click data streams.

Click-Through Rate Prediction Continual Learning

AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020

1 code implementation25 Nov 2021 Jin Xu, Mingjian Chen, Jianqiang Huang, Xingyuan Tang, Ke Hu, Jian Li, Jia Cheng, Jun Lei

Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications.

Graph Classification Node Classification

Polarized skylight orientation determination artificial neural network

no code implementations6 Jul 2021 Huaju Liang, Hongyang Bai, Ke Hu, Xinbo Lv

This paper proposes an artificial neural network to determine orientation using polarized skylight.

Learning Word-Level Confidence For Subword End-to-End ASR

no code implementations11 Mar 2021 David Qiu, Qiujia Li, Yanzhang He, Yu Zhang, Bo Li, Liangliang Cao, Rohit Prabhavalkar, Deepti Bhatia, Wei Li, Ke Hu, Tara N. Sainath, Ian McGraw

We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR).

Automatic Speech Recognition Model Selection

Transformer Based Deliberation for Two-Pass Speech Recognition

no code implementations27 Jan 2021 Ke Hu, Ruoming Pang, Tara N. Sainath, Trevor Strohman

In this work, we explore using transformer layers instead of long-short term memory (LSTM) layers for deliberation rescoring.

Speech Recognition

Textual Echo Cancellation

no code implementations13 Aug 2020 Shaojin Ding, Ye Jia, Ke Hu, Quan Wang

In this paper, we propose Textual Echo Cancellation (TEC) - a framework for cancelling the text-to-speech (TTS) playback echo from overlapping speech recordings.

Acoustic echo cancellation Speech Recognition

A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

no code implementations28 Mar 2020 Tara N. Sainath, Yanzhang He, Bo Li, Arun Narayanan, Ruoming Pang, Antoine Bruguier, Shuo-Yiin Chang, Wei Li, Raziel Alvarez, Zhifeng Chen, Chung-Cheng Chiu, David Garcia, Alex Gruenstein, Ke Hu, Minho Jin, Anjuli Kannan, Qiao Liang, Ian McGraw, Cal Peyser, Rohit Prabhavalkar, Golan Pundak, David Rybach, Yuan Shangguan, Yash Sheth, Trevor Strohman, Mirko Visontai, Yonghui Wu, Yu Zhang, Ding Zhao

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.

Deliberation Model Based Two-Pass End-to-End Speech Recognition

no code implementations17 Mar 2020 Ke Hu, Tara N. Sainath, Ruoming Pang, Rohit Prabhavalkar

End-to-end (E2E) models have made rapid progress in automatic speech recognition (ASR) and perform competitively relative to conventional models.

Automatic Speech Recognition

Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models

no code implementations21 Jun 2019 Ke Hu, Antoine Bruguier, Tara N. Sainath, Rohit Prabhavalkar, Golan Pundak

Contextual automatic speech recognition, i. e., biasing recognition towards a given context (e. g. user's playlists, or contacts), is challenging in end-to-end (E2E) models.

Automatic Speech Recognition

Adversarial Training for Multilingual Acoustic Modeling

no code implementations17 Jun 2019 Ke Hu, Hasim Sak, Hank Liao

In this work, we apply the domain adversarial network to encourage the shared layers of a multilingual model to learn language-invariant features.

Automatic Speech Recognition Language Identification

Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation

1 code implementation WS 2018 Antonio Toral, Sheila Castilho, Ke Hu, Andy Way

We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context.

Machine Translation Translation

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