Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). This is less of a problem in NLP where unsupervised pre-training involves classification over thousands of word types.

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Sebastian Ruder

AUGUST 18, 2019

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks. However, transfer learning is not a recent phenomenon in NLP.

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Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

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Sebastian Ruder

OCTOBER 26, 2019

Cross-lingual learning might be useful—but why should we care about applying NLP to other languages in the first place? The NLP Resource Hierarchy In current machine learning, the amount of available training data is the main factor that influences an algorithm's performance. 2015 , Artetxe et al.,

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Topbots

SEPTEMBER 11, 2023

Of course, there is also the middle ground of reasonably complex problems like concept extraction for specific domains, for which you might consider training a deep neural network from scratch. 3] Don Norman (2013). 4] Google, Gartner and Motista (2013). 2] Orbit Media (2022). We asked 1016 Bloggers. [3]

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The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. They are now the foundation of today’s NLP systems. 2019. . ↩ Strategies for Pre-training Graph Neural Networks.

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Building on seminal work from Kiros et al.

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Probably Approximately a Scientific Blog

AUGUST 30, 2019

This has caused some fear among the general population; some criticism among other NLP people; and many questions from their relatives ("hey, look at this article I've found - did they just solve NLP? The effect is similar to the effect that pre-trained word embeddings had on NLP in 2013.

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Explosion

DECEMBER 17, 2013

I wrote this blog post in 2013, describing an exciting advance in natural language understanding technology. It would be relatively easy to provide a beam-search version of spaCy…But, I think the gap in accuracy will continue to close, especially given advances in neural network learning.

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NLP People

JULY 24, 2019

We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and – guess what – NLP-based analysis of ACL papers from the last 20 years. Neural Networks are the workhorse of Deep Learning (cf.

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Thanks for reading!

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Viso.ai

DECEMBER 21, 2023

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries. Thus, positioning him as one of the top AI influencers in the world.

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ODSC - Open Data Science

FEBRUARY 29, 2024

One of their notable recent projects is a new interpretability method for training neural networks with sparse, discrete, and controllable hidden states. Similar to the MIT lab, its approach is multidisciplinary to enhance human-robot interactions using the power of AI.

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