High-performance KNN similarity functions in Python, optimized for sparse matrices
A fast and highly accurate differentiable Top-k operator from the "Successive Halving Top-k Operator" AAAI'21 paper.
[ICML2022] Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations [AAAI-2020]
Algorithms for traversing spatial networks and performing top-k queries.
A single-file, JIT-compiled CUDA batch top-k with variable k and variable sequence length per row.
Empirical study of the Privacy–Robustness–Performance trilemma in Federated Learning: combining DP-SGD, FLTrust Byzantine-robust aggregation, and Top-k compression across 8 configurations on MNIST, simulated with Flower.
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