_id,doi,title
536,10.1007/s00446-017-0315-1,Communication-efficient randomized consensus
5961,10.1145/3212734.3212798,A brief tutorial on distributed and concurrent machine learning
5962,10.1145/3212734.3212763,The convergence of stochastic gradient descent in asynchronous shared memory
5963,10.1145/3212734.3212756,Relaxed schedulers can efficiently parallelize iterative algorithms
5964,10.1145/3212734.3212785,Brief Announcement: Performance prediction for coarse-grained locking
5965,10.1145/3210377.3210411,Distributionally linearizable data structures
5966,10.1145/3210377.3210406,The transactional conflict problem
6001,10.1145/3201897,ThreadScan: Automatic and scalable memory reclamation
6031,10.1109/SiPS.2018.8598402,Fast quantized arithmetic on x86: Trading compute for data movement
6558,,Byzantine stochastic gradient descent
6589,,The convergence of sparsified gradient methods
397,10.1145/3178487.3178489,Harnessing epoch-based reclamation for efficient range queries
43,10.1073/pnas.1721061115,Model of bacterial toxin-dependent pathogenesis explains infective dose
791,10.1145/3087801.3087810,The power of choice in priority scheduling
487,10.1145/3143361.3143367,Towards unlicensed cellular networks in TV white spaces
431,,QSGD: Communication-efficient SGD via gradient quantization and encoding
432,,"ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning"