Online learning: A comprehensive survey
TLDR
Online learning as mentioned in this paper is a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time.About:
This article is published in Neurocomputing.The article was published on 2021-10-12 and is currently open access. It has received 234 citations till now. The article focuses on the topics: Online machine learning & Unsupervised learning.read more
Citations
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Journal ArticleDOI
A Survey on Deep Learning for Named Entity Recognition
TL;DR: A comprehensive review on existing deep learning techniques for NER is provided in this paper, where the authors systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder.
Journal ArticleDOI
Machine Learning and Integrative Analysis of Biomedical Big Data.
Bilal Mirza,Wei Wang,Jie Wang,Howard Choi,Neo Christopher Chung,Neo Christopher Chung,Peipei Ping +6 more
TL;DR: In this article, state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.
Journal ArticleDOI
Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety
TL;DR: Four opportunities for research directed toward clinical relevance are identified: exploring intermediate outcomes and underlying disease mechanisms; focusing on purposes that are likely to be used in clinical practice; anticipating quality and safety barriers to adoption; and exploring the potential for digital personalized medicine arising from the integration of digital phenotyping and digital interventions.
Journal ArticleDOI
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
TL;DR: In this article, a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme were used to address the limitations of deep artificial neural networks, which have revolutionized the computer vision domain.
Proceedings ArticleDOI
Continual Lifelong Learning in Natural Language Processing: A Survey
TL;DR: This work looks at the problem of CL through the lens of various NLP tasks, and discusses major challenges in CL and current methods applied in neural network models.
References
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Reinforcement Learning: An Introduction
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TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
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Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.