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Found 6 papers, 3 papers with code
Enhancing Instance-Level Image Classification with Set-Level Labels
Our experimental results demonstrate the effectiveness of our approach, showcasing improved classification performance compared to traditional single-instance label-based methods.
An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis
Recently, Swarm Intelligence-related LFA models have been proposed and adopted widely to improve the optimization process of LFA with high efficiency, i. e., the Particle Swarm Optimization (PSO)-LFA model.
A Dynamic-Neighbor Particle Swarm Optimizer for Accurate Latent Factor Analysis
High-Dimensional and Incomplete matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis model.
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
To scale up the computation of queries to large batches, we further propose an efficient batch-mode acquisition procedure, which aims to maximize a novel information measure defined through the acquisition function.
Evaluating and interpreting caption prediction for histopathology images
The automatic generation of captions from medical images can provide for an efficient way to annotate histopathology images with natural language descriptions.
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0. 5.
