What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Deep learning, Speech recognition and Benchmark.
His study in Artificial neural network and Feature learning falls under the purview of Artificial intelligence.
His Feature learning research incorporates elements of Feature extraction and Unsupervised learning.
His research on Speech recognition frequently connects to adjacent areas such as Character recognition.
Adam Coates has researched Benchmark in several fields, including InfiniBand, Supercomputer, Distributed computing and Pattern recognition.
His work on Vector quantization as part of general Pattern recognition study is frequently linked to Basis function, bridging the gap between disciplines.
His most cited work include:
- Reading Digits in Natural Images with Unsupervised Feature Learning (2704 citations)
- An analysis of single-layer networks in unsupervised feature learning (1744 citations)
- Deep speech 2: end-to-end speech recognition in English and mandarin (1307 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Speech recognition, Artificial neural network, Pattern recognition and Deep learning.
Adam Coates has included themes like Machine learning and Computer vision in his Artificial intelligence study.
His Language model study in the realm of Speech recognition interacts with subjects such as Task, Mandarin Chinese and Key.
His work on Connectionism as part of general Artificial neural network study is frequently linked to Component, therefore connecting diverse disciplines of science.
His research in Pattern recognition intersects with topics in Perspective and Word.
Adam Coates combines subjects such as CUDA and Distributed computing with his study of Deep learning.
He most often published in these fields:
- Artificial intelligence (68.42%)
- Speech recognition (31.58%)
- Artificial neural network (28.07%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (68.42%)
- Speech recognition (31.58%)
- Artificial neural network (28.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Speech recognition, Artificial neural network, Language model and Pattern recognition.
Artificial intelligence is closely attributed to Machine learning in his work.
His work on Autoencoder and Discriminative model as part of general Machine learning research is often related to Marginal distribution, Prior probability and Kernel density estimation, thus linking different fields of science.
His Speech recognition research includes elements of Representation and Benchmark.
His work on Connectionism as part of his general Artificial neural network study is frequently connected to Component, thereby bridging the divide between different branches of science.
His Language model study integrates concerns from other disciplines, such as Closed captioning, Natural language and Machine translation.
Between 2016 and 2021, his most popular works were:
- Deep Voice: Real-time Neural Text-to-Speech (140 citations)
- Exploring neural transducers for end-to-end speech recognition (116 citations)
- Cold Fusion: Training Seq2Seq Models Together with Language Models. (98 citations)
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
