Aria
@aria_mt_nguyen
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Joined February 2018
Latest preprint: "Parameter Inference from a Non-stationary Unknown Process" (PINUP) We're really interested in the problem of inferring sources of non-stationary variation directly from measured time-series data. https://t.co/7KI02eTFpO Quick summary 👇
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I'e very excited about our AI latest paper, which shows that an architecture radically different from standard neural network achieves much better accuracy with fewer parameters for interesting physics and math problems. arXiv:2402.05110
MLPs are so foundational, but are there alternatives? MLPs place activation functions on neurons, but can we instead place (learnable) activation functions on weights? Yes, we KAN! We propose Kolmogorov-Arnold Networks (KAN), which are more accurate and interpretable than MLPs.🧵
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The one-page math paper (with two references) that won John Nash the Nobel
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New pre-print by @aria_mt_nguyen w @jlizier: "A feature-based information-theoretic approach for detecting interpretable, long-timescale pairwise interactions from time series" Introduces a new method: uses features to infer time-series interactions https://t.co/k7AU7agbTh
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