[MINI] One Shot Learning
Data Skeptic22 Sep 2017

[MINI] One Shot Learning

One Shot Learning is the class of machine learning procedures that focuses learning something from a small number of examples. This is in contrast to "traditional" machine learning which typically requires a very large training set to build a reasonable model.

In this episode, Kyle presents a coded message to Linhda who is able to recognize that many of these new symbols created are likely to be the same symbol, despite having extremely few examples of each. Why can the human brain recognize a new symbol with relative ease while most machine learning algorithms require large training data? We discuss some of the reasons why and approaches to One Shot Learning.

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Avsnitt(601)

Mapping Dialects with Twitter Data

Mapping Dialects with Twitter Data

When users on Twitter post with geographic tags, it creates the opportunity for a variety of interesting questions to be posed having to do with language, dialects, and location.  In this episode, Kyl...

26 Apr 201925min

Sentiment Analysis

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20 Apr 201927min

Attention Primer

Attention Primer

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Cross-lingual Short-text Matching

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5 Apr 201924min

ELMo

ELMo

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29 Mars 201923min

BLEU

BLEU

Bilingual evaluation understudy (or BLEU) is a metric for evaluating the quality of machine translation using human translation as examples of acceptable quality results. This metric has become a wide...

23 Mars 201942min

Simultaneous Translation at Baidu

Simultaneous Translation at Baidu

While at NeurIPS 2018, Kyle chatted with Liang Huang about his work with Baidu research on simultaneous translation, which was demoed at the conference.

15 Mars 201924min

Human vs Machine Transcription

Human vs Machine Transcription

Machine transcription (the process of translating audio recordings of language to text) has come a long way in recent years. But how do the errors made during machine transcription compare to the erro...

8 Mars 201932min

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