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D-Index & Metrics

Computer Science

D-Index
48
Citations
32261
World Ranking
5994
National Ranking
2697

Overview

Ruoming Pang is a researcher affiliated with Google in the United States, specializing in computer science with a focus on artificial intelligence and signal processing. Their research encompasses a variety of topics within speech recognition and audio processing, demonstrating significant contributions in these areas.

The main fields of study for Ruoming Pang include:

  • Computer Science

Within this broad field, their subfields of study are:

  • Artificial Intelligence
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Hardware and Architecture

Their work covers several main topics, such as:

  • Speech Recognition and Synthesis
  • Music and Audio Processing
  • Speech and Audio Processing
  • Natural Language Processing Techniques
  • Topic Modeling
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning

Ruoming Pang has a substantial publication record, with frequent appearances in venues like arXiv (Cornell University) and several IEEE conferences. Prominent publication venues include:

  • arXiv (Cornell University)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
  • Interspeech 2022
  • IEEE Journal of Selected Topics in Signal Processing

Notable recent papers authored or co-authored by Ruoming Pang include:

  • "Conformer: Convolution-augmented Transformer for Speech Recognition" (2020, arXiv (Cornell University))
  • "w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training" (2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU))
  • "Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition" (2020, arXiv (Cornell University))
  • "BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition" (2022, IEEE Journal of Selected Topics in Signal Processing)
  • "Vector-quantized Image Modeling with Improved VQGAN" (2021, arXiv (Cornell University))

Collaborations with frequent co-authors include work with the following researchers:

  • Tara N. Sainath
  • Chung-Cheng Chiu
  • Yonghui Wu
  • Wei Han
  • James Qin

Best Publications

  • EfficientDet: Scalable and Efficient Object Detection

    Mingxing Tan;Ruoming Pang;Quoc V. Le

  • Searching for MobileNetV3

    Andrew Howard;Ruoming Pang;Hartwig Adam;Quoc Le

  • MnasNet: Platform-Aware Neural Architecture Search for Mobile

    Mingxing Tan;Bo Chen;Ruoming Pang;Vijay Vasudevan

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

    Jonathan Shen;Ruoming Pang;Ron J. Weiss;Mike Schuster

  • Searching for MobileNetV3.

    Andrew Howard;Mark Sandler;Grace Chu;Liang-Chieh Chen

  • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

    Ye Jia;Yu Zhang;Ron J. Weiss;Quan Wang

  • Streaming End-to-end Speech Recognition for Mobile Devices

    Yanzhang He;Tara N. Sainath;Rohit Prabhavalkar;Ian McGraw

  • Characteristics of internet background radiation

    Ruoming Pang;Vinod Yegneswaran;Paul Barford;Vern Paxson

  • NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

    Golnaz Ghiasi;Tsung-Yi Lin;Ruoming Pang;Quoc V. Le

  • w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

    Unknown

  • Oblivious hashing: A stealthy software integrity verification primitive

    Yuqun Chen;Ramwarathnam Venkatesan;Matthew Cary;Ruoming Pang

  • The devil and packet trace anonymization

    Ruoming Pang;Mark Allman;Vern Paxson;Jason Lee

  • ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

    Wei Han;Zhengdong Zhang;Yu Zhang;Jiahui Yu

  • A first look at modern enterprise traffic

    Ruoming Pang;Mark Allman;Mike Bennett;Jason Lee

  • Reliability and security in the CoDeeN content distribution network

    Limin Wang;Kyoung Soo Park;Ruoming Pang;Vivek Pai

  • Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models

    Jiahui Yu;Pengchong Jin;Hanxiao Liu;Gabriel Bender

  • Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

    Yu Zhang;James Qin;Daniel S. Park;Wei Han

  • A high-level programming environment for packet trace anonymization and transformation

    Ruoming Pang;Vern Paxson

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

    Tara N. Sainath;Yanzhang He;Bo Li;Arun Narayanan

Frequent Co-Authors

Yonghui Wu
Yonghui Wu Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Tara N. Sainath
Tara N. Sainath Google (United States)
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Zhifeng Chen
Zhifeng Chen Google (United States)
Patrick Nguyen
Patrick Nguyen Google (United States)
Liangliang Cao
Liangliang Cao Google (United States)
Vijay K. Vasudevan
Vijay K. Vasudevan Google (United States)
Vern Paxson
Vern Paxson University of California, Berkeley

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