Summary of Empirical Influence Functions to Understand the Logic Of Fine-tuning, by Jordan K. Matelsky et al.
Empirical influence functions to understand the logic of fine-tuningby Jordan K. Matelsky, Lyle Ungar, Konrad…
Empirical influence functions to understand the logic of fine-tuningby Jordan K. Matelsky, Lyle Ungar, Konrad…
Adaptive boosting with dynamic weight adjustmentby Vamsi Sai Ranga Sri Harsha ManginaFirst submitted to arxiv…
On the Use of Anchoring for Training Vision Modelsby Vivek Narayanaswamy, Kowshik Thopalli, Rushil Anirudh,…
Contrastive Learning Via Equivariant Representationby Sifan Song, Jinfeng Wang, Qiaochu Zhao, Xiang Li, Dufan Wu,…
Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learningby Shengyu Tao,…
Cross-Table Pretraining towards a Universal Function Space for Heterogeneous Tabular Databy Jintai Chen, Zhen Lin,…
Neural Optimal Transport with Lagrangian Costsby Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon…
Phasor-Driven Acceleration for FFT-based CNNsby Eduardo Reis, Thangarajah Akilan, Mohammed KhalidFirst submitted to arxiv on:…
Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Frameworkby Parsa Moradi, Behrooz Tahmasebi, Mohammad Ali…
Multi-Objective Neural Architecture Search by Learning Search Space Partitionsby Yiyang Zhao, Linnan Wang, Tian GuoFirst…