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28 de jun. de 2001 · Corpus ID: 219683473; Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data @inproceedings{Lafferty2001ConditionalRF, title={Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data}, author={John D. Lafferty and Andrew McCallum and Fernando Pereira}, booktitle={International Conference on Machine Learning}, year={2001}, url ...
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of Data Science, Yale University; john.lafferty@yale.edu. 1 arXiv:2304.00195v3 [stat.ML] 5 Oct 2023. tangled representations encoding a mixture of relational information and object-level features, re-sulting in suboptimal sample-efficiency for learning relations.
Selected Publications: Selective inference for group-sparse linear models, Advances in Neural Information Processing Systems 29, 2016. Fan Yang, Rina Foygel Barber, Prateek Jain, and John Lafferty, arxiv:1607.08211 Local minimax complexity of stochastic convex optimization, Yuancheng Zhu, Sabyasachi Chatterjee, John Duchi and John Lafferty.
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1 de abr. de 2023 · An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the Abstractor. At the core of the Abstractor is a variant of attention called relational cross-attention. The approach is motivated by an architectural inductive bias for relational learning that disentangles relational information from object-level features. This enables explicit ...
John Lafferty, Director of the Center for Neurocomputation and Machine Intelligence + John C. Malone Professor of Statistics and Data Science We develop next-generation computational frameworks for neuroscience, identifying abstractions and creating models that help uncover principles of cognition.
