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Transformers: State-of-the-art Natural Language Processing

On Oct 10, 2019
@jasonyo shared
RT @Thom_Wolf: With 180+ papers mentioning 🤗 Transformers and its predecessors, it was high time to put out a real paper that people could cite. 🥳 🎉 https://t.co/gPMT19sA2J With @LysandreJik @SanhEstPasMoi @julien_c @ClementDelangue @moi_anthony @pierrci @remilouf @MorganFunto @jamieabrew https://t.co/oJT9lbLbyg
Open

Recent advances in modern Natural Language Processing (NLP) research have been dominated by the combination of Transfer Learning methods with large-scale Transformer language models. ...

arxiv.org
On Oct 10, 2019
@jasonyo shared
RT @Thom_Wolf: With 180+ papers mentioning 🤗 Transformers and its predecessors, it was high time to put out a real paper that people could cite. 🥳 🎉 https://t.co/gPMT19sA2J With @LysandreJik @SanhEstPasMoi @julien_c @ClementDelangue @moi_anthony @pierrci @remilouf @MorganFunto @jamieabrew https://t.co/oJT9lbLbyg
Open

Transformers: State-of-the-art Natural Language Processing

Recent advances in modern Natural Language Processing (NLP) research have been dominated by the combination of Transfer Learning methods with large-scale Transformer language models. ...



On Oct 10, 2019
@jeremyphoward shared
RT @ylecun: PyTorch 1.3 is live! Mobile device deployment, model quantization, named tensors, crypto, model interpretability, detectron2... https://t.co/yYQoJqbeUi https://t.co/XbQ08r2c8B
Open

PyTorch 1.3 adds mobile, privacy, quantization, and named tensors

The release of PyTorch 1.3 includes support for model deployment to mobile devices, quantization, and front-end improvements, like the ability to name tensors. We’re also launching tools ...

On Oct 15, 2019
@weballergy shared
RT @OpenAI: We've trained an AI system to solve the Rubik's Cube with a human-like robot hand. This is an unprecedented level of dexterity for a robot, and is hard even for humans to do. The system trains in an imperfect simulation and quickly adapts to reality: https://t.co/O04izt3KvO https://t.co/8lGhU2pPck
Open

Solving Rubik’s Cube with a Robot Hand

We've trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand.

On Oct 9, 2019
@stanfordnlp shared
RT @bobehayes: 10 Free Top Notch Natural Language Processing Courses Includes: @fastdotai @Stanford @UnivOxford @UW @yandexcom @coursera @UCBerkeley @TelAvivUni @_inesmontani https://t.co/cp6wuHE86s #NLP #MachineLearning https://t.co/eMLNwvH5i0
Open

10 Free Top Notch Natural Language Processing Courses

Are you looking to learn natural language processing? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its ...

On Oct 9, 2019
@fchollet shared
RT @PyImageSearch: I just published my (free) 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV! 🚀🔥 Includes step-by-step instructions on: - Getting Started - Face Applications - Object Detection - OCR - Embedded/IoT - ...and more! Check it out here: https://t.co/g1I2TyeBSI 👍 https://t.co/W02uBFmOvA
Open

Need help getting started with Computer Vision, Deep Learning, and OpenCV?

Your step-by-step guide to getting started, getting good, and mastering Computer Vision, Deep Learning, and OpenCV.

On Oct 11, 2019
@petewarden shared
RT @thinkmariya: We’ve summarized the key ideas of the research paper “AutoML: the survey of the state-of-the-art” from the Hong Kong Baptist University. Check it out to know, on which tasks AutoML already outperforms human-designed models. #AI #AutoML #ML https://t.co/3Perrkg1zc
Open

What’s State Of The Art In AutoML in 2019?

Following the structure of the original paper, we’ll touch upon the available AutoML techniques, summarize existing approaches to Neural Architecture Search (NAS), provide you with the ...

On Oct 12, 2019
@peteskomoroch shared
RT @GatsbyUCL: Interested in doing a PhD with us? Applications for our 2020 PhD programme in Theoretical and Computational Neuroscience and Machine Learning are open! For more info see https://t.co/5BeI5R8BWA Deadline is the 17th of November.
Open

Gatsby PhD Programme

Training in theoretical and computational neuroscience and machine learning Application for 2020 entry is open. The Gatsby Unit is a centre for theoretical neuroscience and machine ...

On Oct 10, 2019
@pabbeel shared
Much looking forward to catching up with friends and colleagues, and making new ones at @CMU_Robotics tomorrow! Seminar at 3pm in 1305 Newell Simon Hall. https://t.co/lVgKUhWmjY
Open

Deep Learning for Robotics

Abstract: Programming robots remains notoriously difficult.  Equipping robots with the ability to learn would by-pass the need for what otherwise often ends up being time-consuming task ...

On Oct 15, 2019
@shakir_za shared
RT @iclr_conf: #ICLR2020 will have a separate day for workshops👨🏽‍🔧. The deadline for proposals is 25 October (just over a week); workshop chairs are Asja Fischer and @syhw. Details of the call online at https://t.co/dIss36s1pW
Open
On Oct 11, 2019
@thinkmariya shared
Comcast has a tremendous amount of streaming data, and still, it was able to build a robust Enterprise AI Platform with open-source solutions like MLFlow and Kubernetes. Check out the details. #AI #ML https://t.co/7zR7OqJJTj
Open

How Comcast Handles Vast Amounts of Streaming Data with MLFlow and Kubernetes

Kubernetes, specifically Kubeflow, ArgoCD, and Seldon Core, for model deployment. Data transformation and normalization can be ensured by the Data Transformation pod and the Data ...

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On Oct 15, 2019
@shakir_za shared
RT @vukosi: Go Tsamaya Ke Go Bona: Deep Learning Indaba #3 - A journey just beginning https://t.co/NjOw5iK95v @DeepIndaba #sautiyetu #DLIndaba2019 #Masakhane https://t.co/UnMrhmT97y
Open

Go Tsamaya Ke Go Bona: Deep Learning #3 - A journey just beginning

What is the Deep Learning Indaba?, University of Pretoria

On Oct 9, 2019
@mxlearn shared
[D] Machine Learning : Explaining Uncertainty Bias in Machine Learning https://t.co/I1ptHhhmDk
Open
On Oct 15, 2019
@MSFTResearch shared
“We had a strange theorem that was very, very simple, and somehow, we were smart enough to give it a catchy name.” Guest speaker Léon Bottou of @Facebook talks “convexity à la carte” and what it says about approximation properties and global minimization: https://t.co/3rDGg7VdES
Open

AI Institute “Geometry of Deep Learning” 2019 [Day 1 | Session 3]

Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as ...

On Oct 15, 2019
@WIREDScience shared
Viral memes. Gaffe-prone presidential debates. TikTok remixes. You could spend the rest of your life trying to watch YouTube videos. Researchers want to let artificial intelligence algorithms watch and make sense of it instead. https://t.co/5nf8hEdS2w
Open

This Technique Can Make It Easier for AI to Understand Videos

A staggering amount of video is shared online. Researchers are teaching artificial intelligence to process more—while using less power.

On Oct 10, 2019
@peteskomoroch shared
Latest issue of the Projects To Know newsletter #8 from @sarahcat21 @AmplifyPartners: Task-Relevant Adversarial Imitation Learning, Topical-Chat, FaceForensics++ https://t.co/xpybPBthSY
Open

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Devoted to machine learning and data science, Projects to Know is an essential weekly newsletter for anyone who wants keeps tabs on the latest research, open source projects and industry ...

On Oct 10, 2019
@peteskomoroch shared
RT @matvelloso: PyTorch 1.3 https://t.co/PTGlMj5z0g
Open

PyTorch 1.3 comes with speed gains from quantization and TPU support

The latest version of Facebook's open source deep learning library PyTorch comes with quantization, named tensors, and Google Cloud TPU support.

On Oct 14, 2019
@DeepMindAI shared
Transferability is a major challenge in street navigation. Our work @ICCV19 proposed a cross-view policy learning approach that utilises top-down aerial view imagery to enable agents to learn faster and better in unseen street areas. https://t.co/wRgWdbyXeR https://t.co/9sHHMj2X1r
Open

Click here to read the article

We further re- formulate the transfer learning paradigm into three stages: 1) cross-modal training, when the agent is initially trained on multiple city regions, 2) aerial view-only ...

On Oct 11, 2019
@mxlearn shared
[R] Benchmarking Batch Deep Reinforcement Learning Algorithms https://t.co/covD5Yx9qn
Open