1.8k citations · 2.1k across the 6 of their papers we have counts for
7 papers
Scalable and Performant Data Loading
Moto Hira, Christian Puhrsch, Valentin Andrei +11
We present SPDL (Scalable and Performant Data Loading), an open-source, framework-agnostic library designed for efficiently loading array data to GPU. Data loading is often a bottl…
TorchAudio: Building Blocks for Audio and Speech Processing
Yao-Yuan Yang, Moto Hira, Zhaoheng Ni +20
This document describes version 0.10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain. The objective of TorchAudio is to a…
Advances in Pre-Training Distributed Word Representations
Tomas Mikolov, Edouard Grave, Piotr Bojanowski +2
Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl.…
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
Ronan Collobert, Christian Puhrsch, Gabriel Synnaeve
This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding. It is trained to output letters,…
Very Deep Multilingual Convolutional Neural Networks for LVCSR
Tom Sercu, Christian Puhrsch, Brian Kingsbury +1
Convolutional neural networks (CNNs) are a standard component of many current state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, CNNs in LVCS…
Learning Invariants using Decision Trees
Siddharth Krishna, Christian Puhrsch, Thomas Wies
The problem of inferring an inductive invariant for verifying program safety can be formulated in terms of binary classification. This is a standard problem in machine learning: gi…
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
David Eigen, Christian Puhrsch, Rob Fergus
Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relation…