Results 101 to 110 of about 2,509,053 (235)
Quasi Manhattan Wasserstein Distance
, 2023 The Quasi Manhattan Wasserstein Distance (QMWD) is a metric designed to quantify the dissimilarity between two matrices by combining elements of the Wasserstein Distance with specific transformations. It offers improved time and space complexity compared to the Manhattan Wasserstein Distance (MWD) while maintaining accuracy.
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People and Nature, EarlyView.Abstract High aggregate levels of wildlife consumption in cities in Central Africa highlight the need for solutions that balance wildlife protection, local livelihoods and the relational values between people and nature. This study explores the impacts of demand‐ and supply‐side interventions on wild meat consumption through two randomized control ...
Abdoulaye Cisse +2 more
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Sliced Wasserstein Distance for Learning Gaussian Mixture Models
, 2017 Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algorithm for estimating the GMM parameters.
Hoffmann, Heiko +2 more
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Wasserstein distance is a key metric for quantifying data divergence from a distributional perspective. However, its application in privacy-sensitive environments, where direct sharing of raw data is prohibited, presents significant challenges. Existing approaches, such as Differential Privacy and Federated Optimization, have been employed to estimate ...
Li, Wenqian, Pang, Yan
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Dynamic Adaptive Label Assignment for Tiny Object Detection in Remote Sensing Images
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT With the development of unmanned aerial vehicle and satellite technology, the application of tiny object detection in remote sensing images is becoming increasingly widespread. Although significant progress has been made in the accuracy and speed of object detection in recent years, performance declines sharply when general object detectors ...
Shuohao Shi, Qiang Fang, Xin Xu
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AbstractA fundamental problem in metric algebraic geometry is distance minimization.
Paul Breiding +2 more
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AGT: Efficient Offline Reinforcement Learning With Advantage‐Guided Transformer
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Offline reinforcement learning (RL) is a paradigm that seeks to train policies directly based on fixed datasets derived from previous interactions with the environment. However, offline RL faces critical challenges in environments characterised by sparse rewards and datasets dominated by suboptimal trajectories.
Jiaye Wei +4 more
wiley +1 more source
Bridging classical data assimilation and optimal transport: the 3D-Var case [PDF]
Nonlinear Processes in GeophysicsBecause optimal transport (OT) acts as displacement interpolation in physical space rather than as interpolation in value space, it can avoid double-penalty errors generated by mislocations of geophysical fields.
M. Bocquet +4 more
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Sliced Wasserstein Kernel for Persistence Diagrams [PDF]
, 2017 Persistence diagrams (PDs) play a key role in topological data analysis (TDA), in which they are routinely used to describe topological properties of complicated shapes.
Carrière, Mathieu +2 more
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CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Intrinsic motivation serves as the predominant paradigm of exploration in reinforcement learning. In pursuit of an informative and robust state representation, the behavioural metric groups behaviourally equivalent states together, which share the same single‐step reward and transition distribution.
Anjie Zhu +3 more
wiley +1 more source