850 citations
- University of OxfordGB80 papers
- Tsinghua UniversityCN79 papers
- University of ManchesterGB78 papers
- University of Chinese Academy of SciencesCN75 papers
- Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di FrascatiIT72 papers
- Uppsala UniversitySE72 papers
- Shandong UniversityCN70 papers
- University of Science and Technology of ChinaCN70 papers
- University of TarapacáCL69 papers
- Justus-Liebig-Universität GießenDE67 papers
- Johannes Gutenberg University MainzDE66 papers
- Nanjing UniversityCN65 papers
22 papers · 1 filter
Cap the Gap: Solving the Egoistic Dilemma under the Transaction Fee-Incentive Bitcoin
Hongwei Shi, Shengling Wang, Qin Hu +2
Bitcoin has witnessed a prevailing transition that employing transaction fees paid by users rather than subsidy assigned by the system as the main incentive for mining.
Guiding Neural Network Initialization via Marginal Likelihood Maximization
Anthony S. Tai, Chunfeng Huang
We propose a simple, data-driven approach to help guide hyperparameter selection for neural network initialization. We leverage the relationship between neural network and Gaussian…
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
Jiahua Dong, Yang Cong, Gan Sun +3
Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most exist…
Whose hand is this? Person Identification from Egocentric Hand Gestures
Satoshi Tsutsui, Yanwei Fu, David Crandall
Recognizing people by faces and other biometrics has been extensively studied in computer vision. But these techniques do not work for identifying the wearer of an egocentric (firs…
Efficient Competitive Self-Play Policy Optimization
Yuanyi Zhong, Yuan Zhou, Jian Peng
Reinforcement learning from self-play has recently reported many successes. Self-play, where the agents compete with themselves, is often used to generate training data for iterati…
Accelerated solving of coupled, non-linear ODEs through LSTM-AI
Camila Faccini de Lima, Juliano Ferrari Gianlupi, John Metzcar +1
The present project aims to use machine learning, specifically neural networks (NN), to learn the trajectories of a set of coupled ordinary differential equations (ODEs) and decrea…