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Found 7 papers, 2 papers with code
Mitigating Privacy Risks in LLM Embeddings from Embedding Inversion
Embeddings have become a cornerstone in the functionality of large language models (LLMs) due to their ability to transform text data into rich, dense numerical representations that capture semantic and syntactic properties.
Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis
In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis.
Complex Locomotion Skill Learning via Differentiable Physics
Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers.
An Experimental Analysis of Indoor Spatial Queries: Modeling, Indexing, and Processing
Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries.
Databases Data Structures and Algorithms
Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network
Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important for a practical computer-aided diagnosis system on retinal images.
Kernel Machines With Missing Responses
For the quadratic loss, we then propose a family of doubly-robust kernel machines.
Towards Real-time Simulation of Hyperelastic Materials
In this paper, we show that Projective Dynamics can be interpreted as a quasi-Newton method.
Graphics
