arXiv AI
KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
2d ago
Recursive Sparse Attention: Million-Token Length Extrapolation at Logarithmic Cost
arXiv:2611. 07232v1 Announce Type: cross Abstract: At context lengths of about 1M tokens, dense attention is costly in prefill and decoding, and training typically relies on long documents, which become scarcer as the target length increases.
arXiv Machine Learning
Aug 3
POSSE-kNN: Pathwise Out-of-Bag Selected Subspace Ensembles for Binary Classification
arXiv:2211. 11278v3 Announce Type: replace-cross Abstract: Nearest neighbour classification is attractive for tabular data, but its performance can deteriorate when a fixed query centred neighbourhood does not follow the local class geometry.
arXiv AI
Aug 18
Static Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps
arXiv:2608. 16309v1 Announce Type: cross Abstract: Static pruning is widely used to accelerate sparse neural retrieval, yet existing studies each validate their conclusions within a single custom pipeline, leaving it unclear which findings transfer to modern engines with different index organizations and dynamic pruning mechanisms.
arXiv Machine Learning
Aug 7
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
arXiv:2409. 12788v3 Announce Type: replace Abstract: Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric.
arXiv AI
Jun 30
Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon
arXiv:2605. 09708v2 Announce Type: replace-cross Abstract: We present Metal-Sci, a 10-task benchmark of scientific Apple Silicon Metal compute kernels spanning six optimization regimes (stencils, all-pairs in $n$-body problems, multi-field Boltzmann, neighbor-list molecular dynamics, multi-kernel PDE, FFT).
arXiv AI
Jul 15
How Query Visibility Changes KV-Cache Compression Rankings: A Matched-Budget Audit
arXiv:2607. 11942v1 Announce Type: cross Abstract: KV-cache compression methods are predominantly evaluated with the query appended to the context before compression -- a query-aware protocol.