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Found 3 papers, 3 papers with code
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Modeling long-range dependencies across sequences is a longstanding goal in machine learning and has led to architectures, such as state space models, that dramatically outperform Transformers on long sequences.
Graph Neural Networks Use Graphs When They Shouldn't
We analyze the implicit bias of gradient-descent learning of GNNs and prove that when the ground truth function does not use the graphs, GNNs are not guaranteed to learn a solution that ignores the graph, even with infinite data.
ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM
In this paper, we present ASOCEM (Automatic Segmentation Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which requires as an input only the approximated particle size.
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