_id,doi,title
6676,10.1145/3313276.3316407,Why extension-based proofs fail
6759,10.37236/8096,On grounded L-graphs and their relatives
6931,10.4230/LIPICS.DISC.2019.29,Byzantine approximate agreement on graphs
6933,10.1145/3293611.3331633,Fast approximate shortest paths in the congested clique
6935,10.1145/3293611.3331581,Does preprocessing help under congestion?
6936,10.1111/ecog.04444,What can observational data reveal about metacommunity processes?
6972,10.1145/3339471,Self-stabilising Byzantine clock synchronisation is almost as easy as consensus
7122,10.1109/cdc.2018.8619625,Gradient compression for communication-limited convex optimization
7201,10.1145/3295500.3356222,SparCML: High-performance sparse communication for machine learning
7214,10.1186/s12859-019-3208-4,Recovering rearranged cancer chromosomes from karyotype graphs
7228,10.1007/978-3-030-29400-7_23,Scalable FIFO channels for programming via communicating sequential processes
7437,,Distributed learning over unreliable networks
7542,,Powerset convolutional neural networks
5947,10.1145/3288599.3288617,A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries
6485,10.1145/3293883.3297000,Lock-free channels for programming via communicating sequential processes
7812,,Model compression via distillation and quantization
85,10.1007/978-3-319-96983-1_33,Snapshot based synchronization: A fast replacement for Hand-over-Hand locking
7116,10.5441/002/EDBT.2018.14,Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study
7123,10.1137/1.9781611975031.144,Space-optimal majority in population protocols
76,10.1007/s00446-018-0342-6,Near-optimal self-stabilising counting and firing squads