REALM: Retrieval-Augmented Language Model Pre ... - Kenton L

three popular Open-QA benchmarks, and find that we outperform all previous methods by a significant margin (4-16% absolute accuracy), while also providing qualitative benefits such as interpretability and modularity. 1. Introduction Recent advances in language model pre-training have shown that models such as BERT (Devlin et al.,2018),

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Chapter 3 O&M Management - Energy

www1.eere.energy.gov

Training, and Administration, form the basis for a solid O&M organization, the key lies in the ... Traditional thinking in the O&M ield focused on a single metric, reliability, for program ... tracking and trending metrics against industry benchmarks (NASA 2000). Table 3.1.1. Industry O&M metrics and benchmarks .

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Understanding deep learning requires rethinking ... - arXiv

arxiv.org

remarkably small difference between training and test performance. Conventional ... In this work, we problematize the traditional view of generalization by showing that it is incapable ... standard architectures trained on the CIFAR10 and ImageNet classication benchmarks. While

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Domain-Adversarial Training of Neural Networks

www.jmlr.org

results on traditional deep learning image data sets|such as MNIST (LeCun et al., 1998) and SVHN (Netzer et al., 2011)|as well as on Office benchmarks (Saenko et al., 2010), where domain-adversarial learning allows obtaining a deep architecture that considerably improves over previous state-of-the-art accuracy.

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Performance Monitoring Indicators - MEASURE Evaluation

www.measureevaluation.org

Indicator benchmarks and international comparators 19 ... Further, the task force pointed out that the Bank’s traditional method of appraisal and evaluation of development impact—the calculation of economic rate of return or ... training. indicators: C. performance. Performance.

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arXiv:2005.14165v4 [cs.CL] 22 Jul 2020

arxiv.org

Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples.

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BERT: Pre-training of Deep Bidirectional Transformers for ...

aclanthology.org

pre-training for language representations. Un-likeRadford et al.(2018), which uses unidirec-tional language models for pre-training, BERT uses masked language models to enable pre-trained deep bidirectional representations. This is also in contrast toPeters et al.(2018a), which uses a shallow concatenation of independently

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arXiv:1408.5882v2 [cs.CL] 3 Sep 2014

arxiv.org

ple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the ar-chitecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which

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Unsupervised Data Augmentation for Consistency ... - NeurIPS

proceedings.neurips.cc

Unsupervised Data Augmentation for Consistency Training Qizhe Xie 1, 2, Zihang Dai , Eduard Hovy , Minh-Thang Luong , Quoc V. Le1 1 Google Research, Brain Team, 2 Carnegie Mellon University {qizhex, dzihang, hovy}@cs.cmu.edu, {thangluong, qvl}@google.com Abstract Semi-supervised learning lately has shown much promise in improving deep learn-

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