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Found 6 papers, 2 papers with code
Deep Support Vectors
While the success of deep learning is commonly attributed to its theoretical equivalence with Support Vector Machines (SVM), the practical implications of this relationship have not been thoroughly explored.
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping
To evaluate the performance of large AI vision models, especially Meta's Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model.
Any-Way Meta Learning
Furthermore, we argue that the inherent label equivalence naturally lacks semantic information.
Assessment of a new GeoAI foundation model for flood inundation mapping
Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem solving and geographic knowledge discovery, because of their potential to enable powerful image analysis by learning and extracting important image features from vast amounts of geospatial data.
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
To address this issue, we propose a novel URL framework that causally predicts future states while increasing the dimension of the latent manifold by decorrelating the features in the latent space.
JORLDY: a fully customizable open source framework for reinforcement learning
Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields.
