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Found 13 papers, 3 papers with code
Cold Fusion: Training Seq2Seq Models Together with Language Models
Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition.
Exploring Neural Transducers for End-to-End Speech Recognition
In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition.
Principled Hybrids of Generative and Discriminative Domain Adaptation
This provides us a very general way to interpolate between generative and discriminative extremes through different choices of priors.
Reducing Bias in Production Speech Models
Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition.
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Keyword spotting (KWS) constitutes a major component of human-technology interfaces.
Deep Voice: Real-time Neural Text-to-Speech
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks.
Active Learning for Speech Recognition: the Power of Gradients
For speech recognition, confidence scores and other likelihood-based active learning methods have been shown to be effective.
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.
An Empirical Evaluation of Deep Learning on Highway Driving
We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection.
Ranked #2 on
Lane Detection
on Caltech Lanes Cordova
Deep Speech: Scaling up end-to-end speech recognition
We present a state-of-the-art speech recognition system developed using end-to-end deep learning.
Deep learning for class-generic object detection
We investigate the use of deep neural networks for the novel task of class generic object detection.
Emergence of Object-Selective Features in Unsupervised Feature Learning
Recent work in unsupervised feature learning has focused on the goal of discovering high-level features from unlabeled images.
Selecting Receptive Fields in Deep Networks
Recent deep learning and unsupervised feature learning systems that learn from unlabeled data have achieved high performance in benchmarks by using extremely large architectures with many features (hidden units) at each layer.
