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Found 10 papers, 2 papers with code
A Note on Optimizing the Ratio of Monotone Supermodular Functions
We show that for the problem of minimizing (or maximizing) the ratio of two supermodular functions, no bounded approximation ratio can be achieved via polynomial number of queries, if the two supermodular functions are both monotone non-decreasing or non-increasing.
Optimal Algorithm and Lower Bound for Submodular Maximization
In this work, we present the first linear query complexity algorithm for maximizing a monotone submodular function subject to a cardinality constraint, which achieves the approximation ratio of $(1-1/e-\varepsilon)$ using $O(n/\varepsilon)$ queries.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.
N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients
Plantar pressure is one of which contains this information and it describes human walking features.
A Model for Medical Diagnosis Based on Plantar Pressure
Here, we propose a model using convolutional neural network based on plantar pressure for medical diagnosis.
ICFVR 2017: 3rd International Competition on Finger Vein Recognition
The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets.
Learning to Design Games: Strategic Environments in Reinforcement Learning
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment.
A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification
Our work is first to define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion method incorporating Weighted Voting.