Search Results for author:
Found 3 papers, 2 papers with code
DataDAM: Efficient Dataset Distillation with Attention Matching
Emerging research on dataset distillation aims to reduce training costs by creating a small synthetic set that contains the information of a larger real dataset and ultimately achieves test accuracy equivalent to a model trained on the whole dataset.
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Motivated by the success of previous work co-designing machine learning and MPC, we develop an activation function co-design.
Local-Global Vectors to Improve Unigram Terminology Extraction
The present paper explores a novel method that integrates efficient distributed representations with terminology extraction.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
