Category: Machine Learning

Articles About Machine Learning

GRAPH EMBEDDING, Machine Learning

Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning

Wikipedia is edited by volunteer editors around the world. Considering the large amount of existing content (e.g. over 5M articles in English Wikipedia), deciding what to edit next can be difficult, both for experienced users that usually have a huge backlog of articles to prioritize, as well as for newcomers who that might need guidance in selecting the next article to contribute… Therefore, helping editors to find relevant articles should improve their performance and help in the retention of new […]

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LANGUAGE MODELLING, Machine Learning

Grounded Compositional Outputs for Adaptive Language Modeling

Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains and tasks. A language model’s emph{vocabulary}—typically selected before training and permanently fixed later—affects its size and is part of what makes it resistant to such adaptation… Prior work has used compositional input embeddings based on surface forms to ameliorate this issue. In this work, we go one step beyond and […]

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Machine Learning

Secure Data Sharing With Flow Model

In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data. We consider a variant of this problem, where the input data can be shared for machine learning training purposes, but the data are also encrypted so that they cannot be recovered by other parties… We present a rotation based method using flow model, and theoretically justified its security. We demonstrate the effectiveness of our method in different scenarios, including supervised secure model […]

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DATA AUGMENTATION, Machine Learning

Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences

Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose to formulate word sense disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair ranking task to select the most probable sense definition given a context sentence and a list of candidate sense definitions… We also introduce a data augmentation technique for WSD using existing example sentences from WordNet. […]

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Machine Learning

What is Machine Learning?

Last Updated on August 16, 2020 You’re interested in Machine Learning and maybe you dabble in it a little. If you talk about Machine Learning with a friend or colleague one day, you run the risk of someone actually asking you: “So, what is machine learning?“ The goal of this post is to give you a few definitions to think about and a handy one-liner definition that is easy to remember. We will start out by getting a feeling for […]

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Machine Learning

Practical Machine Learning Problems

Last Updated on January 20, 2018 What is Machine Learning? We can read authoritative definitions of machine learning, but really, machine learning is defined by the problem being solved. Therefore the best way to understand machine learning is to look at some example problems. In this post we will first look at some well known and understood examples of machine learning problems in the real world. We will then look at a taxonomy (naming system) for standard machine learning problems […]

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Machine Learning

Best Machine Learning Resources for Getting Started

Last Updated on August 16, 2020 This was a really hard post to write because I want it to be really valuable. I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonized over what to include and what to exclude. I had to work hard to put myself in the […]

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Machine Learning

Programmers Can Get Into Machine Learning

Last Updated on August 16, 2020 In this post I want to show you that programmers can get into machine learning. I will show you that learning machine learning can be just like learning any other piece of high technology. We’ll compare learning machine learning to learning to program in the first place, which may have been an even larger challenge. Image license some rights reserved by iwannt A Designer Wants to Code Pretend you are a designer, say a […]

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Machine Learning

Why Get Into Machine Learning?

Last Updated on September 27, 2016 Discover Your Personal Why And Finally Get Unstuck In this post, we will explore why you are interested in machine learning. We will look at some questions that can help you get to the root of what draws you to the field. We will finish with a map showing the 4 main “whys” so that you identify where you fit and what resources to target. Question Your Why Why are you interested in machine […]

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Machine Learning

Self-Study Guide to Machine Learning

Last Updated on August 16, 2020 There are lots of things you can do to learn about machine learning. There are resources like books and courses you can follow, competitions you can enter and tools you can use. In this post I want to put some structure around these activities and suggest a loose ordering of what to tackle when in your journey from programmer to machine learning master. Four Levels of Machine Learning Consider four levels of competence in […]

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