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Artificial Intelligence

Top news of the week: 18.05.2022.

#Datascience
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#ai
#covid19

Research

@xamat shared
On May 12, 2022
"AI-driven product innovation: from Recommender Systems to COVID-19" by @xamat #ai #covid19 https://t.co/cBEyWAyGAa - slides from my recent class at @StanfordMBA
Open
AI-driven product innovation: from Recommender Systems to COVID-19

AI-driven product innovation: from Recommender Systems to COVID-19

AI/Machine Learning has become an integral part of many household tech products, from Netflix to our phones. In this talk I will draw from my experience drivin…

@mxlearn shared
On May 14, 2022
[D] AI stocks https://t.co/37hUK64anb
Open
[D] AI stocks

[D] AI stocks

0 votes and 2 comments so far on Reddit

@weballergy shared
On May 14, 2022
RT @Zixin_Wen: Excited to share our new work on understanding contrastive learning without negative pairs (e.g. BYOL and SimSiam), joint with Yuanzhi Li. https://t.co/c9hGMSJ1Bd We directly characterize its training dynamics and uncover the mechanism of the trainable prediction head. 1/n https://t.co/87Xch52jTR
Open
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning

The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning

Recently the surprising discovery of the Bootstrap Your Own Latent (BYOL) method by Grill et al. shows the negative term in contrastive loss can be removed if we add the so-called ...

@glouppe shared
On May 13, 2022
RT @scikit_learn: scikit-learn 1.1 is out! What's new? You can check the release highlights there: https://t.co/VtlEthbUwR pip install -U scikit-learn or conda install -c conda-forge scikit-learn #sklearn #ML #Datascience #opensource #Python https://t.co/MEZX7fkpTR
Open
Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1

We are pleased to announce the release of scikit-learn 1.1! Many bug fixes and improvements were added, as well as some new key features. We detail below a few of the major features of this ...

@mxlearn shared
On May 12, 2022
[D] is it easy to transition to another field after PhD? https://t.co/xX7mXROaQo
Open
[D] is it easy to transition to another field after PhD?

[D] is it easy to transition to another field after PhD?

15 votes and 14 comments so far on Reddit

@hardmaru shared
On May 16, 2022
RT @SchmidhuberAI: 10th anniversary: in 2012, our feedforward neural network called the DanNet was the first to win an image segmentation competition—namely, the ISBI Brain Image Challenge. Today, many are using similar networks for all kinds of image segmentation https://t.co/INrXsotZGR
Open
2022: 10th anniversary: in March 2012, our feedforward neural network was the first to win an image segmentation competition: the ISBI Challenge

2022: 10th anniversary: in March 2012, our feedforward neural network was the first to win an image segmentation competition: the ISBI Challenge

In March 2012, when compute was roughly 100 times more expensive than in 2022, our Deep Learning Neural Network[DL1-6a] (NN), called the DanNet,[DAN,DAN1][GPUCNN1-3,5-8] won the …

@kastnerkyle shared
On May 16, 2022
RT @jordiponsdotme: 🥸 New post! ICASSP 2022 – my learnings https://t.co/24PqC1efjv https://t.co/glO9tG8Lh3
Open
ICASSP 2022 – my learnings4 min read

ICASSP 2022 – my learnings4 min read

General trends Let’s learn to predict the parameters of systems that are well-known by audio and synth nerds. Audio companies are interested in amateur music recordings …

@ylecun shared
On May 16, 2022
RT @PyTorch: The Intel engineers in the PyTorch open-source community have created an new Intel® Extension for PyTorch* which maximizes deep learning inference and training performance on Intel CPUs. Get the extension to make use of these features today: @fanzhao_intel https://t.co/RMtyhRHeDE
Open
Accelerating PyTorch with Intel® Extension for PyTorch*

Accelerating PyTorch with Intel® Extension for PyTorch*

Authors: Fan Zhao, Jiong Gong, Eikan Wang

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