Summary of Application-driven Innovation in Machine Learning, by David Rolnick et al.
Application-Driven Innovation in Machine Learningby David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L.…
Application-Driven Innovation in Machine Learningby David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L.…
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNNby Yingtao Shen, Minqing…
Imitating Cost-Constrained Behaviors in Reinforcement Learningby Qian Shao, Pradeep Varakantham, Shih-Fen ChengFirst submitted to arxiv…
A Unified Kernel for Neural Network Learningby Shao-Qun Zhang, Zong-Yi Chen, Yong-Ming Tian, Xun LuFirst…
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fieldsby Bangchen Yin, Yue Yin, Yuda W. Tang,…
Certified Machine Unlearning via Noisy Stochastic Gradient Descentby Eli Chien, Haoyu Wang, Ziang Chen, Pan…
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseasesby Yumeng Yang, Ashley Gilliam,…
Less Is More – On the Importance of Sparsification for Transformers and Graph Neural Networks…
Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Languageby Alistair Plum,…
Sanity Checks for Explanation Uncertaintyby Matias Valdenegro-Toro, Mihir MulyeFirst submitted to arxiv on: 25 Mar…