His primary areas of investigation include Artificial intelligence, Deep learning, Machine learning, Pattern recognition and Computer vision. The study of Artificial intelligence is intertwined with the study of Set in a number of ways. His research in Deep learning tackles topics such as Initialization which are related to areas like Scale and Robustness.
Graham W. Taylor combines subjects such as Pixel, Context, Pose and Field with his study of Machine learning. His Pattern recognition study integrates concerns from other disciplines, such as Image and Enhanced Data Rates for GSM Evolution. He usually deals with Computer vision and limits it to topics linked to Activity recognition and Feature, Visualization, Boltzmann machine and Unsupervised learning.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Deep learning, Pattern recognition and Convolutional neural network. His work in Artificial intelligence addresses issues such as Computer vision, which are connected to fields such as Classifier. When carried out as part of a general Machine learning research project, his work on Unsupervised learning, Regularization and Semi-supervised learning is frequently linked to work in Metric and Focus, therefore connecting diverse disciplines of study.
Graham W. Taylor has researched Deep learning in several fields, including Cognitive neuroscience of visual object recognition, Bayesian optimization, Re identification and Gesture recognition. His study ties his expertise on Image together with the subject of Pattern recognition. His research integrates issues of Contextual image classification and MNIST database in his study of Convolutional neural network.
His primary scientific interests are in Artificial intelligence, Machine learning, Pattern recognition, Theoretical computer science and Artificial neural network. As part of his studies on Artificial intelligence, Graham W. Taylor frequently links adjacent subjects like Computer vision. His Computer vision study incorporates themes from Visual processing, Robustness and Transformer.
His Machine learning research integrates issues from Initialization, Radiomics and Code. His work carried out in the field of Theoretical computer science brings together such families of science as Scene graph, Bilinear interpolation, Nonparametric bayesian, Statistical model and Range. His Artificial neural network research incorporates elements of Combinatorial optimization and Hyperparameter.
His main research concerns Artificial intelligence, Machine learning, Initialization, Focus and Deep learning. His studies deal with areas such as Ranking and Pattern recognition as well as Artificial intelligence. His Pattern recognition study combines topics in areas such as Contextual image classification, Pascal, Graph and Multigraph.
His Machine learning study integrates concerns from other disciplines, such as Consistency, Fréchet distance and Joint probability distribution. His Initialization study combines topics from a wide range of disciplines, such as Coherence, Context, Annotation and Graph neural networks. His study in Convolutional neural network is interdisciplinary in nature, drawing from both MNIST database and Domain knowledge.
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.
Improved Regularization of Convolutional Neural Networks with Cutout.
Terrance Devries;Graham W. Taylor.
arXiv: Computer Vision and Pattern Recognition (2017)
Matthew D. Zeiler;Dilip Krishnan;Graham W. Taylor;Rob Fergus.
computer vision and pattern recognition (2010)
Adaptive deconvolutional networks for mid and high level feature learning
Matthew D. Zeiler;Graham W. Taylor;Rob Fergus.
international conference on computer vision (2011)
Modeling Human Motion Using Binary Latent Variables
Graham W. Taylor;Geoffrey E. Hinton;Sam T. Roweis.
neural information processing systems (2006)
Convolutional learning of spatio-temporal features
Graham W. Taylor;Rob Fergus;Yann LeCun;Christoph Bregler.
european conference on computer vision (2010)
The Recurrent Temporal Restricted Boltzmann Machine
Ilya Sutskever;Geoffrey E. Hinton;Graham W. Taylor.
neural information processing systems (2008)
Factored conditional restricted Boltzmann Machines for modeling motion style
Graham W. Taylor;Geoffrey E. Hinton.
international conference on machine learning (2009)
Deep Multimodal Learning: A Survey on Recent Advances and Trends
Dhanesh Ramachandram;Graham W. Taylor.
IEEE Signal Processing Magazine (2017)
Antibodies to human serum amyloid P component eliminate visceral amyloid deposits
Karl Bodin;Stephan Ellmerich;Melvyn C. Kahan;Glenys A. Tennent.
Learning Confidence for Out-of-Distribution Detection in Neural Networks.
Terrance DeVries;Graham W. Taylor.
arXiv: Machine Learning (2018)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: