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Found 6 papers, 1 papers with code
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images
Ophthalmic images and derivatives such as the retinal nerve fiber layer (RNFL) thickness map are crucial for detecting and monitoring ophthalmic diseases (e. g., glaucoma).
Deep Learning for Bias Detection: From Inception to Deployment
To create a more inclusive workplace, enterprises are actively investing in identifying and eliminating unconscious bias (e. g., gender, race, age, disability, elitism and religion) across their various functions.
Towards an Interpretable Latent Space in Structured Models for Video Prediction
We work with models which are object-centric, i. e., explicitly work with object representations, and propagate a loss in the latent space.
COVID-19 Screening Using Residual Attention Network an Artificial Intelligence Approach
Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2).
Control over Skies: Survivability, Coverage, and Mobility Laws for Hierarchical Aerial Base Stations
Aerial Base Stations (ABSs) have gained significant importance in the next generation of wireless networks for accommodating mobile ground users and flash crowds with high convenience and quality.
Lifted Marginal MAP Inference
Lifted inference reduces the complexity of inference in relational probabilistic models by identifying groups of constants (or atoms) which behave symmetric to each other.
