b'Zhifeng Chen'

research

∙ 06/01/2023

Contrastive Learning MRI Reconstruction

Purpose: We propose a novel contrastive learning latent space representa...

0 Mevan Ekanayake, et al. ∙

research

∙ 05/29/2023

Brainformers: Trading Simplicity for Efficiency

Transformers are central to recent successes in natural language process...

0 Yanqi Zhou, et al. ∙

research

∙ 05/20/2023

Lifelong Language Pretraining with Distribution-Specialized Experts

Pretraining on a large-scale corpus has become a standard method to buil...

0 Wuyang Chen, et al. ∙

research

∙ 03/29/2023

MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks

The development of language models have moved from encoder-decoder to de...

0 Weicheng Kuo, et al. ∙

research

∙ 02/20/2023

Computing persistent homology by spanning trees and critical simplices

Topological data analysis can extract effective information from higher-...

0 Dinghua Shi, et al. ∙

research

∙ 02/17/2023

Massively Multilingual Shallow Fusion with Large Language Models

While large language models (LLM) have made impressive progress in natur...

0 Ke Hu, et al. ∙

research

∙ 02/08/2023

Noise2Music: Text-conditioned Music Generation with Diffusion Models

We introduce Noise2Music, where a series of diffusion models is trained ...

0 Qingqing Huang, et al. ∙

research

∙ 07/13/2022

N-Grammer: Augmenting Transformers with latent n-grams

Transformer models have recently emerged as one of the foundational mode...

7 Aurko Roy, et al. ∙

research

∙ 05/09/2022

Building Machine Translation Systems for the Next Thousand Languages

In this paper we share findings from our effort to build practical machi...

8 Ankur Bapna, et al. ∙

research

∙ 02/18/2022

Mixture-of-Experts with Expert Choice Routing

Sparsely-activated Mixture-of-experts (MoE) models allow the number of p...

0 Yanqi Zhou, et al. ∙

research

∙ 12/13/2021

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Scaling language models with more data, compute and parameters has drive...

4 Nan Du, et al. ∙

research

∙ 09/27/2021

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

We summarize the results of a host of efforts using giant automatic spee...

1 Yu Zhang, et al. ∙

research

∙ 06/15/2021

Scene Transformer: A unified multi-task model for behavior prediction and planning

Predicting the future motion of multiple agents is necessary for plannin...

7 Jiquan Ngiam, et al. ∙

research

∙ 05/10/2021

GSPMD: General and Scalable Parallelization for ML Computation Graphs

We present GSPMD, an automatic, compiler-based parallelization system fo...

4 Yuanzhong Xu, et al. ∙

research

∙ 03/30/2021

3D-MAN: 3D Multi-frame Attention Network for Object Detection

3D object detection is an important module in autonomous driving and rob...

0 Zetong Yang, et al. ∙

research

∙ 03/02/2021

Pseudo-labeling for Scalable 3D Object Detection

To safely deploy autonomous vehicles, onboard perception systems must wo...

0 Benjamin Caine, et al. ∙

research

∙ 03/01/2021

Scalable Scene Flow from Point Clouds in the Real World

Autonomous vehicles operate in highly dynamic environments necessitating...

0 Philipp Jund, et al. ∙

research

∙ 01/03/2021

Computing Cliques and Cavities in Networks

Complex networks have complete subgraphs such as nodes, edges, triangles...

0 Dinghua Shi, et al. ∙

research

∙ 06/30/2020

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Neural network scaling has been critical for improving the model quality...

7 Dmitry Lepikhin, et al. ∙

research

∙ 05/04/2020

Streaming Object Detection for 3-D Point Clouds

Autonomous vehicles operate in a dynamic environment, where the speed wi...

6 Wei Han, et al. ∙

research

∙ 03/28/2020

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

Thus far, end-to-end (E2E) models have not been shown to outperform stat...

0 Tara N. Sainath, et al. ∙

research

∙ 12/10/2019

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

The research community has increasing interest in autonomous driving res...

0 Pei Sun, et al. ∙

research

∙ 12/10/2019

Scalability in Perception for Autonomous Driving: An Open Dataset Benchmark

The research community has increasing interest in autonomous driving res...

0 Pei Sun, et al. ∙

research

∙ 11/06/2019

A comparison of end-to-end models for long-form speech recognition

End-to-end automatic speech recognition (ASR) models, including both att...

0 Chung-Cheng Chiu, et al. ∙

research

∙ 09/11/2019

Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model

Multilingual end-to-end (E2E) models have shown great promise in expansi...

26 Anjuli Kannan, et al. ∙

research

∙ 08/29/2019

StarNet: Targeted Computation for Object Detection in Point Clouds

LiDAR sensor systems provide high resolution spatial information about t...

0 Jiquan Ngiam, et al. ∙

research

∙ 07/11/2019

Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

We introduce our efforts towards building a universal neural machine tra...

0 Naveen Arivazhagan, et al. ∙

research

∙ 07/09/2019

Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning

We present a multispeaker, multilingual text-to-speech (TTS) synthesis m...

0 Yu Zhang, et al. ∙

research

∙ 04/12/2019

Direct speech-to-speech translation with a sequence-to-sequence model

We present an attention-based sequence-to-sequence neural network which ...

0 Ye Jia, et al. ∙

research

∙ 04/05/2019

LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

This paper introduces a new speech corpus called "LibriTTS" designed for...

0 Heiga Zen, et al. ∙

research

∙ 02/21/2019

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

Lingvo is a Tensorflow framework offering a complete solution for collab...

13 Jonathan Shen, et al. ∙

research

∙ 11/16/2018

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

GPipe is a scalable pipeline parallelism library that enables learning o...

0 Yanping Huang, et al. ∙

research

∙ 10/16/2018

Hierarchical Generative Modeling for Controllable Speech Synthesis

This paper proposes a neural end-to-end text-to-speech (TTS) model which...

0 Wei-Ning Hsu, et al. ∙

research

∙ 06/12/2018

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

We describe a neural network-based system for text-to-speech (TTS) synth...

0 Ye Jia, et al. ∙

research

∙ 04/26/2018

The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation

The past year has witnessed rapid advances in sequence-to-sequence (seq2...

0 Mia Xu Chen, et al. ∙

research

∙ 12/16/2017

Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions

This paper describes Tacotron 2, a neural network architecture for speec...

0 Jonathan Shen, et al. ∙

research

∙ 12/06/2017

An analysis of incorporating an external language model into a sequence-to-sequence model

Attention-based sequence-to-sequence models for automatic speech recogni...

0 Anjuli Kannan, et al. ∙

research

∙ 12/05/2017

No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models

For decades, context-dependent phonemes have been the dominant sub-word ...

0 Tara N. Sainath, et al. ∙

research

∙ 12/05/2017

Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models

Sequence-to-sequence models, such as attention-based models in automatic...

0 Rohit Prabhavalkar, et al. ∙

research

∙ 12/05/2017

Improving the Performance of Online Neural Transducer Models

Having a sequence-to-sequence model which can operate in an online fashi...

0 Tara N. Sainath, et al. ∙

research

∙ 12/05/2017

State-of-the-art Speech Recognition With Sequence-to-Sequence Models

Attention-based encoder-decoder architectures such as Listen, Attend, an...

0 Chung-Cheng Chiu, et al. ∙

research

∙ 12/05/2017

Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model

Sequence-to-sequence models provide a simple and elegant solution for bu...

0 Bo Li, et al. ∙

research

∙ 03/29/2017

Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages,...

0 Yuxuan Wang, et al. ∙

research

∙ 03/24/2017

Sequence-to-Sequence Models Can Directly Translate Foreign Speech

We present a recurrent encoder-decoder deep neural network architecture ...

0 Ron J. Weiss, et al. ∙

research

∙ 11/14/2016

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

We propose a simple solution to use a single Neural Machine Translation ...

0 Melvin Johnson, et al. ∙

research

∙ 09/26/2016

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Neural Machine Translation (NMT) is an end-to-end learning approach for ...

0 Yonghui Wu, et al. ∙

research

∙ 03/14/2016

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

TensorFlow is an interface for expressing machine learning algorithms, a...

0 Martín Abadi, et al. ∙

Zhifeng Chen

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