HoPA captures high-order cross-modal alignments via a shared proxy to enable scalable omnimodal dataset distillation with better performance-compression trade-offs.
Coverage-centric coreset selection for high pruning rates.arXiv preprint arXiv:2210.15809, 2022
5 Pith papers cite this work. Polarity classification is still indexing.
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GRACE dynamically constructs and updates coresets for LLM training using representation diversity, gradient-based importance, and k-NN graph propagation to improve efficiency and performance.
Electronic structure datasets across materials show high redundancy from low intrinsic dimensionality, allowing pruning to 1/100th size with preserved chemical accuracy.
Large-scale standardized benchmarks show state-of-the-art dataset distillation methods do not outperform coreset selection on ImageNet-scale data and have substantially higher construction costs.
SLAP is a new batch-aware pruning framework that uses distribution-aware stratified sampling and Hessian-approximated gradients to select data, claiming 20-40% less data while matching or exceeding full-dataset performance on LLM instruction tuning tasks.
citing papers explorer
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Omnimodal Dataset Distillation via High-order Proxy Alignment
HoPA captures high-order cross-modal alignments via a shared proxy to enable scalable omnimodal dataset distillation with better performance-compression trade-offs.
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GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization
GRACE dynamically constructs and updates coresets for LLM training using representation diversity, gradient-based importance, and k-NN graph propagation to improve efficiency and performance.
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Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality
Electronic structure datasets across materials show high redundancy from low intrinsic dimensionality, allowing pruning to 1/100th size with preserved chemical accuracy.
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Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?
Large-scale standardized benchmarks show state-of-the-art dataset distillation methods do not outperform coreset selection on ImageNet-scale data and have substantially higher construction costs.
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SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning
SLAP is a new batch-aware pruning framework that uses distribution-aware stratified sampling and Hessian-approximated gradients to select data, claiming 20-40% less data while matching or exceeding full-dataset performance on LLM instruction tuning tasks.