The article proposes a way to incorporate expert rules into machine learning models, specifically in the context of concept-based learning. The authors discuss methods to combine logical rules and neural…
Continue readingThe article presents a reinforcement learning approach to solve the Stochastic Vehicle Routing Problem with Time Windows (SVRP). The goal is to optimize goods delivery by reducing travel costs. The…
Continue readingThe paper introduces a new category of diffusion models built on state space architecture for image data. These models, called Diffusion State Space Models (DiS), treat all inputs including time,…
Continue readingThis study presents three pathways to implementing models and policies in neurosymbolic artificial intelligence (AI), which combines the interpretability and explicit reasoning of symbolic approaches with data-driven neural approaches. The…
Continue readingThis article presents MOCO, a learnable meta optimizer for combinatorial optimization problems, which are often NP-hard. Traditional methods use handcrafted heuristics, but advances in neural networks have motivated the development…
Continue readingThe authors introduce a novel approach to hyper-parameter optimization in machine learning by focusing on the strong convexity of the loss and its flatness. They aim to find hyper-parameter configurations…
Continue readingThis paper offers a different understanding of the Adam optimizer, a popular choice in deep neural network training. Despite its practical success, the theoretical understanding of Adam’s algorithmic components has…
Continue readingThis academic article is about the Factorized Multi-Agent MiniMax Q-Learning (FM3Q), a new framework for two-team zero-sum Markov games. The authors identify inefficiencies in existing methods and propose the individual-global-minimax…
Continue readingThe paper proposes using techniques from control theory to update Deep Neural Networks (DNN) parameters online. It formulates the fully-connected feedforward DNN as a continuous-time dynamical system. The authors present…
Continue readingThe research presents a three-stage training model for intrusion detection in edge computing devices. The model uses advanced deep learning techniques and is designed to work effectively within resource-constrained devices….
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