Jason Weston focuses on Artificial intelligence, Machine learning, Support vector machine, Pattern recognition and Natural language processing. He integrates several fields in his works, including Artificial intelligence and Context. He has researched Machine learning in several fields, including Tree and Classifier.
Jason Weston combines subjects such as Theoretical computer science, Feature selection and Feature vector with his study of Support vector machine. The various areas that Jason Weston examines in his Pattern recognition study include Kernel and Feature scaling. His biological study deals with issues like Training set, which deal with fields such as Attention model, Automatic summarization and Word.
His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Support vector machine and Natural language processing. His study in Question answering, Embedding, Sentence, Semi-supervised learning and Natural language is done as part of Artificial intelligence. His Machine learning research is multidisciplinary, relying on both Beam search and Class.
His research in Pattern recognition intersects with topics in Kernel and Feature. Jason Weston interconnects Data mining, Identification and Feature selection in the investigation of issues within Support vector machine. In the subject of general Natural language processing, his work in Parsing is often linked to Context, thereby combining diverse domains of study.
Artificial intelligence, Human–computer interaction, Conversation, Natural language processing and Machine learning are his primary areas of study. Jason Weston integrates Artificial intelligence and Context in his research. The study incorporates disciplines such as Dialog system, Adventure, Leverage and Set in addition to Human–computer interaction.
As a member of one scientific family, Jason Weston mostly works in the field of Set, focusing on Baseline and, on occasion, Online community and Question answering. His Natural language processing research is multidisciplinary, incorporating elements of Frame and Measure. The concepts of his Machine learning study are interwoven with issues in Beam search and Variety.
Jason Weston mainly focuses on Artificial intelligence, Machine learning, Natural language processing, Human–computer interaction and Transformer. All of his Artificial intelligence and Sentence, Pairwise comparison, Perplexity and Natural language investigations are sub-components of the entire Artificial intelligence study. His Machine learning research is multidisciplinary, incorporating perspectives in Adversarial system, Variety, Natural language understanding and Benchmark.
The Natural language processing study combines topics in areas such as Frame and Measure. His research in the fields of Affordance overlaps with other disciplines such as Conversation. While the research belongs to areas of Transformer, Jason Weston spends his time largely on the problem of Encoder, intersecting his research to questions surrounding Self attention and Security token.
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Gene Selection for Cancer Classification using Support Vector Machines
Isabelle Guyon;Jason Weston;Stephen Barnhill;Vladimir Vapnik.
Machine Learning (2002)
Natural Language Processing (Almost) from Scratch
Ronan Collobert;Jason Weston;Léon Bottou;Michael Karlen.
Journal of Machine Learning Research (2011)
A unified architecture for natural language processing: deep neural networks with multitask learning
Ronan Collobert;Jason Weston.
international conference on machine learning (2008)
Learning with Local and Global Consistency
Dengyong Zhou;Olivier Bousquet;Thomas N. Lal;Jason Weston.
neural information processing systems (2003)
Translating Embeddings for Modeling Multi-relational Data
Antoine Bordes;Nicolas Usunier;Alberto Garcia-Duran;Jason Weston.
neural information processing systems (2013)
Fisher discriminant analysis with kernels
S. Mika;G. Ratsch;J. Weston;B. Scholkopf.
ieee workshop on neural networks for signal processing (1999)
Yoshua Bengio;Jérôme Louradour;Ronan Collobert;Jason Weston.
international conference on machine learning (2009)
A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush;Sumit Chopra;Jason Weston.
empirical methods in natural language processing (2015)
End-to-end memory networks
Sainbayar Sukhbaatar;Arthur Szlam;Jason Weston;Rob Fergus.
neural information processing systems (2015)
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Lukas Käll;Jesse D Canterbury;Jason Weston;William Stafford Noble.
Nature Methods (2007)
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