Starting now! The #CICERObyMetaAI team is answering your questions on Reddit. Ask us anything — join the conversation in the thread on r/machinelearning https://
bit.ly/3VVtoZf
RESEARCH
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Meta AI CICERO Team Hosts Reddit AMA Session
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Google Quantum AI Demonstrates Photon Interaction Using Sycamore
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Under normal conditions, photons do not interact with each other. However, new research by @GoogleQuantumAI using the Sycamore quantum computer demonstrates how microwave photons can be made to interact, forming robust bound states. Check it out at https://
goo.gle/3VZuRx1 -
FNO Integration into Diffusion Models Research
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Indeed, this is very interesting. We have recently incorporated FNO into diffusion models https://
arxiv.org/abs/2211.13449 -
OpenAI Technical Progress Foundation for All Operations
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(all parts of openai do excellent work, but without the technical progress, none of the rest of us would have any reason to be here)
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DeepONets Modification to Function-to-Function Neural Operators
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DeepONets were originally defined on a fixed input grid and hence, do not fall under operators where both inputs and outputs are functions. We can convert it into a neural operator through some modifications. See proposition 5 in https://
arxiv.org/pdf/2108.08481
.pdf
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Full Machine Learning Project Data Visualization with Matplotlib
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Full Machine Learning Project — Data Visualization with Matplotlib (Part 3) #DataScience
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Yoshua Bengio vs Gary Marcus: AI Debate Series
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AI DEBATE : Yoshua Bengio | Gary Marcus Official Video: https://
youtu.be/EeqwFjqFvJA http://
MONTREAL.AI Debates Series #MontrealAI #AIDebate #AGIDebate -
Aleph Alpha’s Underestimated AI Research Deserves More Recognition
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The research work from @Aleph__Alpha in Germany is probably underestimated (they do almost zero marketing)
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Neural Operators for PDEs: Resolution-Independent Function Space Learning
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To solve PDEs, you can learn at a fixed resolution (e.g. with U-net) and interpolate. But error cannot be arbitrarily small this way. Our neural operators are guaranteed to approximate arbitrarily well at any resolution since they learn in function space https://
arxiv.org/abs/2108.08481 -
FastAI Students Explain Diffusion Model Paper
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Another great diffusion model paper explainer from our wonderful @fastdotai students!