Search Results for author:
Found 54 papers, 25 papers with code
Contrastive Learning MRI Reconstruction
We show that this latent space can be utilized for accelerated MR image reconstruction.
Brainformers: Trading Simplicity for Efficiency
Using this insight, we develop a complex block, named Brainformer, that consists of a diverse sets of layers such as sparsely gated feed-forward layers, dense feed-forward layers, attention layers, and various forms of layer normalization and activation functions.
Lifelong Language Pretraining with Distribution-Specialized Experts
Compared to existing lifelong learning approaches, Lifelong-MoE achieves better few-shot performance on 19 downstream NLP tasks.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks.
AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
Model parallelism is conventionally viewed as a method to scale a single large deep learning model beyond the memory limits of a single device.
Massively Multilingual Shallow Fusion with Large Language Models
In this work, we propose to train a single multilingual language model (LM) for shallow fusion in multiple languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Noise2Music: Text-conditioned Music Generation with Diffusion Models
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts.
3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping
Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping.
N-Grammer: Augmenting Transformers with latent n-grams
Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models.
Ranked #5 on
Language Modelling
on C4
Building Machine Translation Systems for the Next Thousand Languages
In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages.
Mixture-of-Experts with Expert Choice Routing
Prior work allocates a fixed number of experts to each token using a top-k function regardless of the relative importance of different tokens.
Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations.
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #26 on
Code Generation
on HumanEval
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Scaling language models with more data, compute and parameters has driven significant progress in natural language processing.
Ranked #8 on
Question Answering
on TriviaQA
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
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 speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Scene Transformer: A unified architecture for predicting multiple agent trajectories
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
GSPMD: General and Scalable Parallelization for ML Computation Graphs
We present GSPMD, an automatic, compiler-based parallelization system for common machine learning computations.
3D-MAN: 3D Multi-frame Attention Network for Object Detection
In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset.
Pseudo-labeling for Scalable 3D Object Detection
To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies.
Scalable Scene Flow from Point Clouds in the Real World
In this work, we introduce a new large-scale dataset for scene flow estimation derived from corresponding tracked 3D objects, which is $\sim$1, 000$\times$ larger than previous real-world datasets in terms of the number of annotated frames.
Computing Cliques and Cavities in Networks
Complex networks contain complete subgraphs such as nodes, edges, triangles, etc., referred to as simplices and cliques of different orders.
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute.
Streaming Object Detection for 3-D Point Clouds
This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to leverage camera-inspired architectures.
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 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.
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
A comparison of end-to-end models for long-form speech recognition
In this paper, we both investigate and improve the performance of end-to-end models on long-form transcription.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model
Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic speech recognition (ASR) coverage of the world's languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
StarNet: Targeted Computation for Object Detection in Point Clouds
We show how our redesign---namely using only local information and using sampling instead of learned proposals---leads to a significantly more flexible and adaptable system: we demonstrate how we can vary the computational cost of a single trained StarNet without retraining, and how we can target proposals towards areas of interest with priors and heuristics.
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges
We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair.
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 model based on Tacotron that is able to produce high quality speech in multiple languages.
Gmail Smart Compose: Real-Time Assisted Writing
In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing.
Direct speech-to-speech translation with a sequence-to-sequence model
We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
This paper introduces a new speech corpus called "LibriTTS" designed for text-to-speech use.
Sound Audio and Speech Processing
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.
Ranked #4 on
Fine-Grained Image Classification
on Birdsnap
(using extra training data)
Hierarchical Generative Modeling for Controllable Speech Synthesis
This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Clone a voice in 5 seconds to generate arbitrary speech in real-time
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures.
Ranked #26 on
Machine Translation
on WMT2014 English-French
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text.
Ranked #1 on
Speech Synthesis
on North American English
An analysis of incorporating an external language model into a sequence-to-sequence model
Attention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units.
Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model
Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network.
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving the Performance of Online Neural Transducer Models
Neural transducer is a streaming sequence-to-sequence model, but has shown a significant degradation in performance compared to non-streaming models such as Listen, Attend and Spell (LAS).
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which corresponds to improving the log-likelihood of the data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Tacotron: Towards End-to-End Speech Synthesis
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.
Ranked #4 on
Speech Synthesis
on North American English
Sequence-to-Sequence Models Can Directly Translate Foreign Speech
We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another.
Machine Translation
Sequence-To-Sequence Speech Recognition
+2
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.
Ranked #35 on
Machine Translation
on WMT2014 English-French
Reward Augmented Maximum Likelihood for Neural Structured Prediction
A key problem in structured output prediction is direct optimization of the task reward function that matters for test evaluation.
TensorFlow: A system for large-scale machine learning
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
