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06/01/2023
Contrastive Learning MRI Reconstruction
Purpose: We propose a novel contrastive learning latent space representa...
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05/29/2023
Brainformers: Trading Simplicity for Efficiency
Transformers are central to recent successes in natural language process...
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05/20/2023
Lifelong Language Pretraining with Distribution-Specialized Experts
Pretraining on a large-scale corpus has become a standard method to buil...
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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...
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02/20/2023
Computing persistent homology by spanning trees and critical simplices
Topological data analysis can extract effective information from higher-...
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02/17/2023
Massively Multilingual Shallow Fusion with Large Language Models
While large language models (LLM) have made impressive progress in natur...
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02/08/2023
Noise2Music: Text-conditioned Music Generation with Diffusion Models
We introduce Noise2Music, where a series of diffusion models is trained ...
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07/13/2022
N-Grammer: Augmenting Transformers with latent n-grams
Transformer models have recently emerged as one of the foundational mode...
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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...
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02/18/2022
Mixture-of-Experts with Expert Choice Routing
Sparsely-activated Mixture-of-experts (MoE) models allow the number of p...
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12/13/2021
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Scaling language models with more data, compute and parameters has drive...
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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...
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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...
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05/10/2021
GSPMD: General and Scalable Parallelization for ML Computation Graphs
We present GSPMD, an automatic, compiler-based parallelization system fo...
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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...
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03/02/2021
Pseudo-labeling for Scalable 3D Object Detection
To safely deploy autonomous vehicles, onboard perception systems must wo...
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03/01/2021
Scalable Scene Flow from Point Clouds in the Real World
Autonomous vehicles operate in highly dynamic environments necessitating...
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01/03/2021
Computing Cliques and Cavities in Networks
Complex networks have complete subgraphs such as nodes, edges, triangles...
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06/30/2020
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Neural network scaling has been critical for improving the model quality...
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05/04/2020
Streaming Object Detection for 3-D Point Clouds
Autonomous vehicles operate in a dynamic environment, where the speed wi...
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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...
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12/10/2019
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
The research community has increasing interest in autonomous driving res...
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12/10/2019
Scalability in Perception for Autonomous Driving: An Open Dataset Benchmark
The research community has increasing interest in autonomous driving res...
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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...
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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...
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08/29/2019
StarNet: Targeted Computation for Object Detection in Point Clouds
LiDAR sensor systems provide high resolution spatial information about t...
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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...
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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...
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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 ...
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04/05/2019
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
This paper introduces a new speech corpus called "LibriTTS" designed for...
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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...
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11/16/2018
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
GPipe is a scalable pipeline parallelism library that enables learning o...
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10/16/2018
Hierarchical Generative Modeling for Controllable Speech Synthesis
This paper proposes a neural end-to-end text-to-speech (TTS) model which...
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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...
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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...
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12/16/2017
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speec...
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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...
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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 ...
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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...
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12/05/2017
Improving the Performance of Online Neural Transducer Models
Having a sequence-to-sequence model which can operate in an online fashi...
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12/05/2017
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Attention-based encoder-decoder architectures such as Listen, Attend, an...
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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...
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03/29/2017
Tacotron: Towards End-to-End Speech Synthesis
A text-to-speech synthesis system typically consists of multiple stages,...
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03/24/2017
Sequence-to-Sequence Models Can Directly Translate Foreign Speech
We present a recurrent encoder-decoder deep neural network architecture ...
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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 ...
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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 ...
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03/14/2016
