1 citations · 1 across the 5 of their papers we have counts for
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It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction
H. Ibrahim Erdogan, Punith Raviswamy, Nikita Agrawal +5
Graph Neural Networks (GNNs) have achieved strong results in molecular property prediction, but polymers present distinct challenges: labeled datasets are scarce and small (typical…
Federated Foundation Language Model Post-Training Should Focus on Open-Source Models
Nikita Agrawal, Ruben Mayer
Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and…
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
Zongxiong Chen, Jiahui Geng, Derui Zhu +5
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset. It is a promising approach to accelerate neural network training and rela…
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Jiahui Geng, Zongxiong Chen, Yuandou Wang +5
Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.…