DOI,IST REx ID,Research Group,Title of publication
10.5194/gmd-17-1563-2024,15097,CaMu,Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
10.1029/2023gl106523,15186,CaMu,"The unreasonable efficiency of total rain evaporation removal in triggering convective self‐aggregation"
10.1029/2023GL106569,15165,CaMu,A pre-monsoon signal of false alarms of Indian monsoon droughts
10.1038/s41612-024-00858-0,18708,CaMu,Exploring causes of distinct regional and subseasonal Indian summer monsoon precipitation responses to CO2 removal
10.5281/ZENODO.10687169,19307,CaMu,Data - The unreasonable efficiency of total rain evaporation removal in triggering convective self-aggregation
10.1145/3658194,17203,ChWo,Spin-it faster: Quadrics solve all topology optimization problems that depend only on mass moments
10.1016/j.cnsns.2024.108011,15313,CaMu,Dynamics and scaling of internally cooled convection
null,17411,ChLa,PEFLL: Personalized federated learning by learning to learn
null,18120,ChLa,Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials
null,18118,ChLa,More flexible PAC-Bayesian meta-learning by learning learning algorithms
10.1109/IPDPS57955.2024.00023,17332,DaAl,Wait-free trees with asymptotically-efficient range queries
null,15011,DaAl,"How to prune your language model: Recovering accuracy on the ""Sparsity May Cry"" benchmark"
10.1109/ICDCS60910.2024.00084,18070,DaAl,Federated SGD with local asynchrony
10.1145/3662158.3662768,17329,"DaAl,KrCh",Game dynamics and equilibrium computation in the population protocol model
null,18121,DaAl,SPADE: Sparsity-guided debugging for deep neural networks
null,18976,DaAl,AsGrad: A sharp unified analysis of asynchronous-SGD algorithms
null,18113,"DaAl,GradSch",Extreme compression of large language models via additive quantization
null,18975,DaAl,Error feedback can accurately compress preconditioners
null,18977,DaAl,SpQR: A sparse-quantized representation for near-lossless LLM weight compression
null,17093,"DaAl,ChLa",Communication-efficient federated learning with data and client heterogeneity