This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial
autoencoders, and their variants. We start with explaining adversarial learning and the …

In recent years, physics-informed neural networks (PINN) have been used to solve stiff-
PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D …

Experience replay is crucial for off-policy reinforcement learning (RL) methods. By
remembering and reusing the experiences from past different policies, experience replay …

Value function factorization methods have become a dominant approach for cooperative
multiagent reinforcement learning under a centralized training and decentralized execution …

Reinforcement learning has demonstrated its capability to solve challenging real-world
problems, ranging from autonomous driving to robotics and planning [1–12]. In some …

We propose the first, to our knowledge, loss function for approximate Nash equilibria of
normal-form games that is amenable to unbiased Monte Carlo estimation. This construction …

In this work, we focus on the challenging task, neuro-disease classification, using functional
magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph …

This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral,
probabilistic, and deep metric learning. We first start with the definition of distance metric …

With the advent of powerful quantum computers, the quest for more efficient quantum
algorithms becomes crucial in attaining quantum supremacy over classical counterparts in …

Fairness in data-driven decision-making studies scenarios where individuals from certain
population segments may be unfairly treated when being considered for loan or job …