Abstract: Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set with the ground-truth label included. However, in a more ...
In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...
Individual survival and evolutionary selection require biological organisms to maximize reward. Economic choice theories define the necessary and sufficient conditions, and neuronal signals of ...
ChatGPT and other AIs could supercharge the influence of lobbyists—but only if we let them Nearly 90% of the multibillion-dollar federal lobbying apparatus in the United States serves corporate ...
Abstract: To improve the topology observability in power distribution networks (PDNs), a two-stage topology identification framework is proposed to recognize the mixed topologies in a large set of ...
Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30–40 years ...
Reverse Osmosis (RO) desalination plants are highly nonlinear multi-input-multioutput systems that are affected by uncertainties, constraints and some physical phenomena such as membrane fouling that ...
The combination of reinforcement learning with deep learning is a promising approach to tackle important sequential decision-making problems that are currently intractable. One obstacle to overcome is ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results