1 code implementation • 22 Apr 2024 • Kamyar Barakati, Hui Yuan, Amit Goyal, Sergei V. Kalinin
We present a methodology based on the concept of a Reward Function coupled with Bayesian Optimization, to optimize image analysis workflows dynamically.
no code implementations • 26 Apr 2023 • Sumit Goel, Amit Goyal
For ratio-form and difference-form contests that admit pure-strategy Nash equilibrium, we find that the effort of both players is monotone decreasing in the probability that ties are broken in favor of the stronger player.
2 code implementations • 15 May 2017 • Wei Lu, Xiaokui Xiao, Amit Goyal, Keke Huang, Laks V. S. Lakshmanan
In a recent SIGMOD paper titled "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study", Arora et al. [1] undertake a performance benchmarking study of several well-known algorithms for influence maximization.
Social and Information Networks
1 code implementation • 4 Jul 2015 • Makoto Yamada, Wenzhao Lian, Amit Goyal, Jianhui Chen, Kishan Wimalawarne, Suleiman A. Khan, Samuel Kaski, Hiroshi Mamitsuka, Yi Chang
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).
no code implementations • 30 Sep 2011 • Amit Goyal, Francesco Bonchi, Laks. V. S. Lakshmanan
In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread.
Databases