Navdeep Jaitly

research

∙ 09/20/2023

Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation

Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be...

0 Ali Mousavi, et al. ∙

research

∙ 09/07/2023

REALM: Robust Entropy Adaptive Loss Minimization for Improved Single-Sample Test-Time Adaptation

Fully-test-time adaptation (F-TTA) can mitigate performance loss due to ...

0 Skyler Seto, et al. ∙

research

∙ 09/06/2023

Robotic Table Tennis: A Case Study into a High Speed Learning System

We present a deep-dive into a real-world robotic learning system that, i...

0 David B. D'Ambrosio, et al. ∙

research

∙ 06/05/2023

PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model

Autoregressive models for text sometimes generate repetitive and low-qua...

2 Yizhe Zhang, et al. ∙

research

∙ 11/11/2022

Continuous Soft Pseudo-Labeling in ASR

Continuous pseudo-labeling (PL) algorithms such as slimIPL have recently...

0 Tatiana Likhomanenko, et al. ∙

research

∙ 11/02/2022

More Speaking or More Speakers?

Self-training (ST) and self-supervised learning (SSL) methods have demon...

0 Dan Berrebbi, et al. ∙

research

∙ 10/17/2022

Continuous Pseudo-Labeling from the Start

Self-training (ST), or pseudo-labeling has sparked significant interest ...

0 Dan Berrebbi, et al. ∙

research

∙ 07/15/2022

Position Prediction as an Effective Pretraining Strategy

Transformers have gained increasing popularity in a wide range of applic...

1 Shuangfei Zhai, et al. ∙

research

∙ 07/05/2022

Efficient Representation Learning via Adaptive Context Pooling

Self-attention mechanisms model long-range context by using pairwise att...

0 Chen Huang, et al. ∙

research

∙ 05/07/2020

RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions

In recent years, all-neural end-to-end approaches have obtained state-of...

0 Chung-Cheng Chiu, et al. ∙

research

∙ 03/31/2020

Robotic Table Tennis with Model-Free Reinforcement Learning

We propose a model-free algorithm for learning efficient policies capabl...

18 Wenbo Gao, et al. ∙

research

∙ 02/20/2020

Imputer: Sequence Modelling via Imputation and Dynamic Programming

This paper presents the Imputer, a neural sequence model that generates ...

0 William Chan, et al. ∙

research

∙ 12/13/2019

SPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping Pong

We introduce a new high resolution, high frame rate stereo video dataset...

12 Steven Schwarcz, 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

∙ 11/30/2018

Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play

Training robots with physical bodies requires developing new methods and...

0 Reza Mahjourian, et al. ∙

research

∙ 08/20/2018

Peptide-Spectra Matching from Weak Supervision

As in many other scientific domains, we face a fundamental problem when ...

4 Samuel S. Schoenholz, 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

∙ 12/05/2017

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

∙ 11/20/2017

Speech recognition for medical conversations

In this paper we document our experiences with developing speech recogni...

0 Chung-Cheng Chiu, et al. ∙

research

∙ 06/16/2017

An online sequence-to-sequence model for noisy speech recognition

Generative models have long been the dominant approach for speech recogn...

0 Chung-Cheng Chiu, et al. ∙

research

∙ 05/16/2017

Learning Hard Alignments with Variational Inference

There has recently been significant interest in hard attention models fo...

0 Dieterich Lawson, et al. ∙

research

∙ 05/14/2017

Discrete Sequential Prediction of Continuous Actions for Deep RL

It has long been assumed that high dimensional continuous control proble...

0 Luke Metz, 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

∙ 12/08/2016

Towards better decoding and language model integration in sequence to sequence models

The recently proposed Sequence-to-Sequence (seq2seq) framework advocates...

0 Jan Chorowski, et al. ∙

research

∙ 10/31/2016

RNN Approaches to Text Normalization: A Challenge

This paper presents a challenge to the community: given a large corpus o...

0 Richard Sproat, et al. ∙

research

∙ 10/10/2016

Latent Sequence Decompositions

We present the Latent Sequence Decompositions (LSD) framework. LSD decom...

0 William Chan, et al. ∙

research

∙ 10/10/2016

Very Deep Convolutional Networks for End-to-End Speech Recognition

Sequence-to-sequence models have shown success in end-to-end speech reco...

0 Yu Zhang, et al. ∙

research

∙ 08/03/2016

Learning Online Alignments with Continuous Rewards Policy Gradient

Sequence-to-sequence models with soft attention had significant success ...

0 Yuping Luo, et al. ∙

research

∙ 05/08/2016

Chained Predictions Using Convolutional Neural Networks

In this paper, we present an adaptation of the sequence-to-sequence mode...

0 Georgia Gkioxari, et al. ∙

research

∙ 11/18/2015

Adversarial Autoencoders

In this paper, we propose the "adversarial autoencoder" (AAE), which is ...

0 Alireza Makhzani, et al. ∙

research

∙ 11/16/2015

A Neural Transducer

Sequence-to-sequence models have achieved impressive results on various ...

0 Navdeep Jaitly, et al. ∙

research

∙ 09/04/2015

Object Recognition from Short Videos for Robotic Perception

Deep neural networks have become the primary learning technique for obje...

0 Ivan Bogun, et al. ∙

research

∙ 08/05/2015

Listen, Attend and Spell

We present Listen, Attend and Spell (LAS), a neural network that learns ...

0 William Chan, et al. ∙

research

∙ 06/09/2015

Pointer Networks

We introduce a new neural architecture to learn the conditional probabil...

0 Oriol Vinyals, et al. ∙

research

∙ 06/09/2015

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Recurrent Neural Networks can be trained to produce sequences of tokens ...

0 Samy Bengio, et al. ∙

research

∙ 04/03/2015

A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

Learning long term dependencies in recurrent networks is difficult due t...

0 Quoc V. Le, et al. ∙

research

∙ 12/22/2014

Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks

Occlusion edges in images which correspond to range discontinuity in the...

0 Soumik Sarkar, et al. ∙

research

∙ 06/04/2014

Multi-task Neural Networks for QSAR Predictions

Although artificial neural networks have occasionally been used for Quan...

0 George E. Dahl, et al. ∙

Navdeep Jaitly

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