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Mbodi AI (YC P25) Is Hiring Founding Machine Learning Engineer (Robotics)
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Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
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Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
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ArrowFlow: Hierarchical Machine Learning in the Space of Permutations
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Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning
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Learning quality scores for chromatin accessibility bigWig tracks using Machine Learning
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Instrumented data for causal scientific machine learning
arXiv:2606.07865v1 Announce Type: cross Abstract: Scientific machine learning is limited less by model size than by the data it is trained on. Observational data records what happened but not why; template synthetic data has a known generating process but only for the simulator's template, not the case a user faces. We argue a third option is now operationally feasible: instrumented data, in which every datum carries the mechanistic model that produced it, an explicit uncertainty over that...
Instrumented data for causal scientific machine learning
arXiv:2606.07865v1 Announce Type: new Abstract: Scientific machine learning is limited less by model size than by the data it is trained on. Observational data records what happened but not why; template synthetic data has a known generating process but only for the simulator's template, not the case a user faces. We argue a third option is now operationally feasible: instrumented data, in which every datum carries the mechanistic model that produced it, an explicit uncertainty over that...
AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials
arXiv:2601.01185v2 Announce Type: replace Abstract: Machine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum accuracy, it is necessary that neighborhoods contained in the training set are rather close to the ones encountered during a simulation. Yet, constructing a single training set that works well for all applications is, and likely will remain, infeasible, so, one...
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