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Found 7 papers, 5 papers with code
Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
We design a Hierarchical Adaptive Alignment model to concurrently learn the fine-grained fragment correspondence between two modalities and align these representations of fragments in three levels.
Functional Protein Design with Local Domain Alignment
The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions.
Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach
The set of local modes and the ridge lines estimated from a dataset are important summary characteristics of the data-generating distribution.
Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data
This paper studies the linear convergence of the subspace constrained mean shift (SCMS) algorithm, a well-known algorithm for identifying a density ridge defined by a kernel density estimator.
The EM Perspective of Directional Mean Shift Algorithm
Under the (generalized) EM framework, we provide a new proof for the ascending property of density estimates and demonstrate the global convergence of directional mean shift sequences.
Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science.
Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications.
