It could be but its most relevant in ML imo
RESEARCH
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Pandemic Impact on Research Confidence: 3000+ Researchers Insights
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Download the full report that offers insights from 3,000+ researchers into how the pandemic has affected confidence in research and how we can best support researchers. #research #researchers #elsevier #CiR #researching #science https://
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Smaller Models with Better Data Can Outperform Larger Ones
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Great point. We are seeing more and more than smaller models with better objectives or data can beat big ones! My main point is that an approach shouldnt go away as models get better. Scale is just one way of getting better
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Reverse Engineering Systems: Breaking Down to Rebuild Better
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Breaking down already built projects, and systems into smaller components and learning them individually to understand how it was engineered then redesign and build it again
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Break Down Projects Into Components Learn Principles Rebuild
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True, break the projects into smaller components, learn each components, understand the principles and build back again
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Paper Review Assessment: Strengths and Weaknesses Analysis
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Correct, some is cut off. The reviewers also give a brief summary of the paper and list two strengths and weaknesses. I don't know the area/work, but at a glance, certainly not the worst review I've ever seen. (Not linking here to avoid a pile-on)
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Marie Curie: Breaking Barriers in Science 100 Years Ago
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17 Nobel Prizes on this iconic picture in 1927 at the Fifth Solvay Conference! Notice that the only woman in this group is Marie Curie. 100 years ago, imagine all the obstacles and biases she had to go through… And you, who is your idol in science? #innovation #tech #science
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Deep Learning’s Future: A 2009 NeurIPS Prediction Recalled
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13 years ago at Neurips 2009, 3 years before AlexNet, a young student @ilyasut explaining to my dinner table why Deep Learning is gonna be the future.
Looking forward to #NeurIPS2022. -
Paper Review Quality Assessment in AI Research
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Correct, some is cut off. The reviewers also give a brief summary of the paper and list two strengths and weaknesses. I don't know the area/work, but at a glance, certainly not the worst review I've ever seen. (Not linking here to avoid a pile-on)
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Overfitting in AI Research: Need Better Global Standards
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2) During lockdown my team and I explored a lot of the GitHubs from China based publishers & found quite a few claiming major breakthroughs to be overfitted. To be fair it also happens with some US researchers. AI needs better research standards globally. Don’t believe the hype!