A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The paper presents a model for predicting corn yield using deep neural networks (DNNR). The model considers the interaction between weather and soil variables, challenging the assumption that there is…
Continue readingThe article discusses a study on an AI-Assisted Provider Platform (A2P2) designed to help care providers deliver protocolized therapies in a more empathetic manner. The platform was tested with providers…
Continue readingPhysics-informed neural network (PINN) is a data-driven solver for differential equations, providing a unified framework to address both forward and inverse problems. However, the complexity of the objective function often…
Continue readingThe largest survey of its kind polled 2,778 AI researchers on their predictions for the future of AI. Participants estimated a 50% chance of AI systems achieving several milestones by…
Continue readingThe article discusses a method for understanding following relations in time series data. The authors formalize a concept of following motifs between two time series and present a framework to…
Continue readingThe article introduces a new framework called H2G2-Net, which uses multi-modal physiological signals to predict cognitive states. The authors argue that existing graph neural networks (GNNs) struggle with this type…
Continue readingThe article introduces Graph2Tac (G2T), a graph neural network that learns hierarchical representations of mathematical concepts in theorem proving. G2T uses a large-scale, graph-based dataset for machine learning in Coq,…
Continue readingThis article discusses the development of DeepSeek LLM, a project dedicated to the advancement of open-source language models. The authors delve into the study of scaling laws and present their…
Continue readingThe paper proposes a solution for two-player general-sum differential games using a Pontryagin-mode neural operator. The authors address the curse of dimensionality and convergence issues through physics-informed neural networks. The…
Continue readingODIN (Omni-Dimensional INstance segmentation) is a model proposed to challenge the belief that 2D and 3D perception require distinct model architectures. ODIN can segment and label both 2D RGB images…
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