Summary of Misam: Using Ml in Dataflow Selection Of Sparse-sparse Matrix Multiplication, by Sanjali Yadav et al.
Misam: Using ML in Dataflow Selection of Sparse-Sparse Matrix Multiplicationby Sanjali Yadav, Bahar AsgariFirst submitted…
Summary of Selecting Interpretability Techniques For Healthcare Machine Learning Models, by Daniel Sierra-botero et al.
Selecting Interpretability Techniques for Healthcare Machine Learning modelsby Daniel Sierra-Botero, Ana Molina-Taborda, Mario S. Valdés-Tresanco,…
Summary of Crafting Parts For Expressive Object Composition, by Harsh Rangwani et al.
Crafting Parts for Expressive Object Compositionby Harsh Rangwani, Aishwarya Agarwal, Kuldeep Kulkarni, R. Venkatesh Babu,…
Summary of Federated Learning with Flexible Architectures, by Jong-ik Park and Carlee Joe-wong
Federated Learning with Flexible Architecturesby Jong-Ik Park, Carlee Joe-WongFirst submitted to arxiv on: 14 Jun…
Summary of Positive-unlabelled Learning For Identifying New Candidate Dietary Restriction-related Genes Among Ageing-related Genes, by Jorge Paz-ruza et al.
Positive-Unlabelled Learning for identifying new candidate Dietary Restriction-related genes among Ageing-related genesby Jorge Paz-Ruza, Alex…
Summary of Learning Solution-aware Transformers For Efficiently Solving Quadratic Assignment Problem, by Zhentao Tan et al.
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problemby Zhentao Tan, Yadong MuFirst submitted to…
Summary of Qqq: Quality Quattuor-bit Quantization For Large Language Models, by Ying Zhang et al.
QQQ: Quality Quattuor-Bit Quantization for Large Language Modelsby Ying Zhang, Peng Zhang, Mincong Huang, Jingyang…
Summary of Clibench: a Multifaceted and Multigranular Evaluation Of Large Language Models For Clinical Decision Making, by Mingyu Derek Ma et al.
CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Makingby Mingyu…
Summary of What Does Softmax Probability Tell Us About Classifiers Ranking Across Diverse Test Conditions?, by Weijie Tu et al.
What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?by Weijie Tu,…
Summary of Pown: Prototypical Open-world Node Classification, by Marcel Hoffmann et al.
POWN: Prototypical Open-World Node Classificationby Marcel Hoffmann, Lukas Galke, Ansgar ScherpFirst submitted to arxiv on:…