We propose a GPU fine-grained load-balancing abstraction that decouples ...
We introduce Stream-K, a work-centric parallelization of matrix
multipli...
Fine-grained workload and resource balancing is the key to high performa...
We identify the graph data structure, frontiers, operators, an iterative...
We consider the problem of learning from training data obtained in diffe...
We address the problem of timing-based localization in wireless networks...
A spatial point process can be characterized by an intensity function wh...
We address the problem of learning a decision policy from observational ...
We introduce a new dataset for multi-class emotion analysis from long-fo...
We study methods for learning sentence embeddings with syntactic structu...
We consider a general statistical learning problem where an unknown frac...
We address the problem of inferring the causal effect of an exposure on ...
We address the problem of predicting spatio-temporal processes with temp...
This work details CipherGAN, an architecture inspired by CycleGAN used f...