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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MINT defines multi-vector search index tuning and provides algorithms that achieve 2.1X to 8.3X latency speedup over baselines under storage and recall constraints.
KiseKloset integrates transformer-based complementary item recommendation, approximate search, and efficient virtual try-on for fashion retrieval and recommendation, reporting 84% user satisfaction in a deployed system.
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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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MINT: Multi-Vector Search Index Tuning
MINT defines multi-vector search index tuning and provides algorithms that achieve 2.1X to 8.3X latency speedup over baselines under storage and recall constraints.
-
KiseKloset for Fashion Retrieval and Recommendation
KiseKloset integrates transformer-based complementary item recommendation, approximate search, and efficient virtual try-on for fashion retrieval and recommendation, reporting 84% user satisfaction in a deployed system.