Tag - survey

by kchonyc on 2020-06-13 (UTC).

this is my latest view on how recurrent units and self-attention are related to each other. beautifully summarized and explained by @SewadeOgun https://t.co/17BAHrQivA
— Kyunghyun Cho (@kchonyc) June 13, 2020

learning survey

by GoogleAI on 2020-06-12 (UTC).

Today we describe a #MachineLearning approach combining #NaturalLanguageProcessing with #ComputerVision to automatically extract data from structured documents—invoices, receipts, etc.—with the potential to streamline many business workflows. Learn more at https://t.co/ed87XgCTbn pic.twitter.com/6uA9GTVzMT
— Google AI (@GoogleAI) June 12, 2020

nlp application learning survey

by JeffDean on 2020-06-09 (UTC).

Nice post about technology behind recent major improvements to Google Translate.

I love the animation showing how much translation quality has improved across so many languages over the past 13 yrs (most recent improvements give +5 BLEU points across all 100+ languages!) https://t.co/GZ7oopWmFr
— Jeff Dean (@🏡) (@JeffDean) June 9, 2020

nlp learning survey

by Reza_Zadeh on 2020-05-18 (UTC).

I can think of hundreds of data augmentation strategies for CV, eg https://t.co/JsGj2fRNMa

For NLP tasks, there's only a handful, eg https://t.co/aRQiZrh5N3

NLP data is far more brittle.
— Reza Zadeh (@Reza_Zadeh) May 18, 2020

nlp learning survey

by lavanyaai on 2020-05-13 (UTC).

Does model size matter? 🤔@jxmorris12 does an excellent job comparing @huggingface's BERT and DistilBERT.#machinelearning #deeplearning #100daysofmlcode https://t.co/Y7dHbH7yqk
— Lavanya 🦋 (@lavanyaai) May 13, 2020

nlp survey learning

by yudapearl on 2020-05-10 (UTC).

A Review of Using Text to Remove Confounding from Causal Estimates: https://t.co/0mNNL8dvL7
In other words, using Text as a noisy proxy for unmeasured confounders, as in https://t.co/fc47co4HOu. A survey of on-going works and pending problems.
— Judea Pearl (@yudapearl) May 10, 2020

research survey

by distillpub on 2020-05-05 (UTC).

Exploring Bayesian Optimization — A new Distill article by @ApoorvAgnihotr2 and @nipun_batra https://t.co/xDvuSjPoFU
— Distill (@distillpub) May 5, 2020

learning survey bayesian

by citnaj on 2020-05-04 (UTC).

I just stumbled upon this Colab by @wightmanr that compares ResNets to EfficientNet. It's super informative and even-handed. It's also consistent with my own experience if you were curious- ResNets remain very competitive (still my go-to). https://t.co/gHH2nw1Ubc
— Jason #Masks4All Antic (@citnaj) May 4, 2020

learning cv survey misc

by thomaskipf on 2020-05-04 (UTC).

My PhD thesis "Deep Learning with Graph-Structured Representations" is now available for download: https://t.co/hyz0cnoewZ -- It covers a range of emerging topics in Deep Learning: from graph neural nets (and graph convolutions) to structure discovery (objects, relations, events) pic.twitter.com/c5nNhsHGxB
— Thomas Kipf (@thomaskipf) May 4, 2020

research survey

by dennybritz on 2020-05-01 (UTC).

Explainable Deep Learning: A Field Guide for the Uninitiated: https://t.co/xBsIfA17Tp

This paper looks like a nice survey for anyone who wants to get started in Deep Learning explainability and trust/safety research. pic.twitter.com/kgwMvDRkQ4
— Denny Britz (@dennybritz) May 1, 2020

learning survey

by karpathy on 2020-04-24 (UTC).

"The Future of Natural Language Processing" https://t.co/PKl4cSSlZ8 from @huggingface, well done quick summary of recent NLP work and lots of good pointers! 👏
— Andrej Karpathy (@karpathy) April 24, 2020

learning video nlp survey

by gneubig on 2020-04-22 (UTC).

Excited to give a (virtual, recorded) talk about "The Low-resource NLP Toolbox, 2020 Version" at the AfricaNLP workshop at #ICLR2020! Slides: https://t.co/ElPMlarUQK

It's somewhat of a birds-eye view, but also focusing heavily on our work at @LTIatCMU. pic.twitter.com/VTQy2bPjXK
— Graham Neubig (@gneubig) April 22, 2020

learning survey

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