Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Leiserson, Charles E."'
Autor:
Bellei, Claudio, Xu, Muhua, Phillips, Ross, Robinson, Tom, Weber, Mark, Kaler, Tim, Leiserson, Charles E., Arvind, Chen, Jie
Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup le
Externí odkaz:
http://arxiv.org/abs/2404.19109
Autor:
Kaler, Tim, Iliopoulos, Alexandros-Stavros, Murzynowski, Philip, Schardl, Tao B., Leiserson, Charles E., Chen, Jie
Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in applications such as recommendation systems and financial forensics.
Externí odkaz:
http://arxiv.org/abs/2305.03152
Autor:
Kaler, Tim, Stathas, Nickolas, Ouyang, Anne, Iliopoulos, Alexandros-Stavros, Schardl, Tao B., Leiserson, Charles E., Chen, Jie
Improving the training and inference performance of graph neural networks (GNNs) is faced with a challenge uncommon in general neural networks: creating mini-batches requires a lot of computation and data movement due to the exponential growth of mul
Externí odkaz:
http://arxiv.org/abs/2110.08450
Arguably, one of the biggest deterrants for software developers who might otherwise choose to write parallel code is that parallelism makes their lives more complicated. Perhaps the most basic problem inherent in the coordination of concurrent tasks
Externí odkaz:
http://hdl.handle.net/1721.1/3692
This paper presents algorithms for the included-sums and excluded-sums problems used by scientific computing applications such as the fast multipole method. These problems are defined in terms of a $d$-dimensional array of $N$ elements and a binary a
Externí odkaz:
http://arxiv.org/abs/2106.00124
Autor:
Weber, Mark, Domeniconi, Giacomo, Chen, Jie, Weidele, Daniel Karl I., Bellei, Claudio, Robinson, Tom, Leiserson, Charles E.
Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency
Externí odkaz:
http://arxiv.org/abs/1908.02591
Autor:
Pareja, Aldo, Domeniconi, Giacomo, Chen, Jie, Ma, Tengfei, Suzumura, Toyotaro, Kanezashi, Hiroki, Kaler, Tim, Schardl, Tao B., Leiserson, Charles E.
Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With th
Externí odkaz:
http://arxiv.org/abs/1902.10191
Autor:
Weber, Mark, Chen, Jie, Suzumura, Toyotaro, Pareja, Aldo, Ma, Tengfei, Kanezashi, Hiroki, Kaler, Tim, Leiserson, Charles E., Schardl, Tao B.
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million peopl
Externí odkaz:
http://arxiv.org/abs/1812.00076
Publikováno v:
Information Processing Letters, 116(2):100-106 (2016)
This paper investigates a variant of the work-stealing algorithm that we call the localized work-stealing algorithm. The intuition behind this variant is that because of locality, processors can benefit from working on their own work. Consequently, w
Externí odkaz:
http://arxiv.org/abs/1804.04773
Publikováno v:
Theory of Computing Systems, 58(2):223-240 (2016)
Inspired by applications in parallel computing, we analyze the setting of work stealing in multithreaded computations. We obtain tight upper bounds on the number of steals when the computation can be modeled by rooted trees. In particular, we show th
Externí odkaz:
http://arxiv.org/abs/1706.03184