A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The study investigates the potential of transferring ‘complementary’ knowledge from one pretrained model to another without performance degradation. This task is complex as additional knowledge can be contained in stronger,…
Continue readingThe article introduces MT-CO 2OL, a new decentralized algorithm for multitask online learning. This algorithm operates in a setting where agents exchange information via a communication network. The regret, or…
Continue readingThis research paper presents a new method, named SAP, to improve Graph Neural Networks (GNNs). GNNs are effective at learning semantics of graph data. However, the structure information of a…
Continue readingThe study explores invariance in neural networks, a feature necessary for many tasks. The authors propose measures to quantify the invariance of neural networks in terms of their internal representation….
Continue readingThe paper presents a new approach to causal inference, utilizing stationary diffusion models. The method is based on learning stochastic differential equations (SDEs) that model a system’s behavior under interventions….
Continue readingThe paper by Joseph Goodier and Neill D. F. Campbell focuses on Out-of-Distribution (OOD) detection with Denoising Diffusion Probabilistic Models (DDPMs). OOD detection is crucial in machine learning to identify…
Continue readingThe article discusses a new method for Collaborative Vehicle Routing (CVR), where delivery companies share information and perform deliveries on behalf of each other to reduce costs and emissions. The…
Continue readingThis research proposed a Bayesian neural controlled differential equation (BNCDE) for estimating treatment effects over time in healthcare. Traditional methods are limited to point estimates, but the BNCDE provides meaningful…
Continue readingThe article explores the use of federated learning in electricity load forecasting. The authors propose a novel transformer-based deep learning approach that improves data privacy by training models locally on…
Continue readingThe article presents a novel approach to collaborative vehicle routing (CVR), a process where carriers share transportation requests to achieve economies of scale. This method reduces costs, greenhouse gas emissions,…
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