jeremy howard google scholar

PDF Restore Delete Forever. "'I wasn't interested in just following the rules': Data scientist Jeremy Howard and the 'next internet'The more images it scans, the more Enlitic's radiology algorithm "learns". New citations to this author. Share to Twitter Share to Facebook Share to Pinterest. The joy of watching a great sportsperson, for example, cannot be dulled by knowing the robot can do the task better. © 2020 Association for Computational Linguistics. Upload PDF. Enter words / phrases / DOI / ISBN / authors / keywords / etc. For a full description of the license, please visit Allen School of Computer Science & Engineering, University of Washington, Seattle, WA. While building on our baseline, we find that pre-training on single segments, instead of two half-length segments with the next sentence prediction (NSP) objective, considerably improves performance on most downstream tasks. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. The following articles are merged in Scholar. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Our method differs from BERT in both the masking scheme and the training objectives. The ones marked * may be different from the article in the profile. "Howard, who has a froth of curls and a humble manner, is an unlikely rock star – a comment first made about him at a November 2015 conference about the technological revolution in healthcare.He appears deeply relaxed, as if he has just finished meditating, with the quiet, precise voice of someone who doesn't need to raise it to be heard.But his new company Enlitic is pioneering an approach to medicine that uses "deep learning" – a game-changing computer technology based on how the human brain operates – to identify cancers in radiology scans faster and more accurately than experienced radiologists.That's just the first step for Enlitic's technology, which Howard says can be applied to just about any disease. Distributed under a CC-BY 4.0 license.https://data.quora.com/First-Quora-Dataset-Release-Question-PairsThe second PASCAL recognising textual entailment challengeSemeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluationKERMIT: Generative insertion-based modeling for sequencesThe PASCAL recognising textual entailment challengeTransformer-XL: Attentive language models beyond a fixed-length contextBERT: Pre-training of deep bidirectional transformers for language understandingAutomatically constructing a corpus of sentential paraphrasesUnified language model pre-training for natural language understanding and generationSearchQA: A new Q&A dataset augmented with context from a search engineMRQA 2019 shared task: Evaluating generalization in reading comprehensionThe third PASCAL recognizing textual entailment challengeJointly predicting predicates and arguments in neural semantic role labelingspaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsingUniversal language model fine-tuning for text classificationpair2vec: Compositional word-pair embeddings for cross-sentence inferenceTriviaQA: A large scale distantly supervised challenge dataset for reading comprehensionBERT for coreference resolution: Baselines and analysisNatural questions: A benchmark for question answering researchHigher-order coreference resolution with coarse-to-fine inferenceLearning recurrent span representations for extractive question answeringMulti-task deep neural networks for natural language understandingRoBERTa: A robustly optimized BERT pretraining approachAn efficient framework for learning sentence representationscontext2vec: Learning generic context embedding with bidirectional LSTMfairseq: A fast, extensible toolkit for sequence modelingCoNLL-2012 shared task: Modeling multilingual unrestricted coreference in ontonotesUsing the output embedding to improve language modelsImproving language understanding with unsupervised learningKnow what you don’t know: Unanswerable questions for SQuADSQuAD: 100,000+ questions for machine comprehension of textMatching the blanks: Distributional similarity for relation learningRecursive deep models for semantic compositionality over a sentiment treebankMASS: Masked sequence to sequence pre-training for language generationBlockwise parallel decoding for deep autoregressive modelsERNIE: Enhanced representation through knowledge integrationGLUE: A multi-task benchmark and analysis platform for natural language understandingA broad-coverage challenge corpus for sentence understanding through inferenceHuggingFace’s Transformers: State-of-the-art natural language processingXLNet: Generalized autoregressive pretraining for language understandingHotpotQA: A dataset for diverse, explainable multi-hop question answeringPosition-aware attention and supervised data improve slot fillingERNIE: Enhanced language representation with informative entities© 2020 Association for Computational Linguistics.

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