Abstract: Time-frequency (TF) analysis is a useful tool for seismic data processing and interpretation. We introduce sparse Bayesian learning (SBL) to TF analysis and propose a new SBL-based ...
Abstract: The performance of the existing sparse Bayesian learning (SBL) methods for off-grid direction-of-arrival (DOA) estimation is dependent on the tradeoff between the accuracy and the ...
Accurate sunlight data is becoming essential for the clean-energy transition, but tracking how much solar radiation reaches ...
Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...
Learning from potential disinformation introduces specific cognitive biases, causing individuals to systematically deviate from an idealized Bayesian updating strategy.
Introduction In 2021, globally, cardiometabolic diseases (CMDs) accounted for 35% of the 1.73 billion disability-adjusted life years (DALYs) attributed to non-communicable diseases. The Healthy Life ...
The state-of-the-art NNs are known to be both computationally and memory intensive, due to the ever-increasing deep structure, i.e., multiple layers with massive neurons and connections (i.e., ...
Parkinson et al., 2025 ) and active-learning loops coupling free-energy calculations with Bayesian acquisition for mutation ranking ( Patel et al., 2024 ). Table 1 summarizes representative models ...
Aniket Wattamwar, Mrunal Kakirwar Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning Tian Xu, Zexuan Chen, Zhilong Zhang, Yi-Chen Li, Chenyang Wang, Lei Yuan, Yang Yu ...
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