What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
George E. Dahl spends much of his time researching Artificial neural network, Artificial intelligence, Hidden Markov model, Mixture model and Speech recognition.
George E. Dahl interconnects Ground truth, Message passing and Benchmark in the investigation of issues within Artificial neural network.
His Artificial intelligence study integrates concerns from other disciplines, such as Quantitative structure and Machine learning.
The Hidden layer research George E. Dahl does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Training, Curvature, Momentum and Schedule, therefore creating a link between diverse domains of science.
His Hidden Markov model research includes themes of Time delay neural network and Margin.
His Pattern recognition research incorporates themes from Deep belief network and Dropout.
His most cited work include:
- Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups (6052 citations)
- On the importance of initialization and momentum in deep learning (2450 citations)
- Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition (2360 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Artificial neural network, Machine learning, Speech recognition and Pattern recognition.
His study in the field of Deep learning, Language model and Sentence is also linked to topics like Vocabulary.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Pipeline and Speedup.
His work on Hidden Markov model and Word error rate as part of general Speech recognition study is frequently linked to FMLLR, bridging the gap between disciplines.
His work deals with themes such as Mixture model, Deep belief network and Discriminative model, which intersect with Hidden Markov model.
His studies deal with areas such as Time delay neural network and Margin as well as Mixture model.
He most often published in these fields:
- Artificial intelligence (66.67%)
- Artificial neural network (48.15%)
- Machine learning (29.63%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (66.67%)
- Artificial neural network (48.15%)
- Machine learning (29.63%)
In recent papers he was focusing on the following fields of study:
George E. Dahl focuses on Artificial intelligence, Artificial neural network, Machine learning, Deep learning and Language model.
He integrates several fields in his works, including Artificial intelligence, Nature versus nurture, Sentinel lymph node, Nodal metastasis, Metastatic breast cancer and Lymph node.
His research in Artificial neural network intersects with topics in Pipeline and Speedup.
His Machine learning study typically links adjacent topics like Training set.
The study incorporates disciplines such as Normalization and Human intelligence in addition to Deep learning.
His Language model research includes elements of Sentence, Automatic summarization and Machine translation.
Between 2017 and 2021, his most popular works were:
- Relational inductive biases, deep learning, and graph networks (891 citations)
- Motivating the Rules of the Game for Adversarial Example Research (124 citations)
- Measuring the Effects of Data Parallelism on Neural Network Training (118 citations)
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