Results 151 to 160 of about 22,594 (260)

ESO: An evolutionary algorithm for efficient model reduction via automated frequency band selection in passive acoustic monitoring

open access: yesMethods in Ecology and Evolution, EarlyView.
Abstract Passive acoustic monitoring (PAM) is an important tool for wildlife monitoring. Deep learning, particularly convolutional neural networks (CNNs), has become the standard approach for developing bioacoustic classifiers. However, real‐time classification remains challenging due to the high computational complexity of these models and the need ...
Lorène Jeantet   +4 more
wiley   +1 more source

Residual permutation tests for feature importance in machine learning

open access: yesBritish Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Psychological research has traditionally relied on linear models to test scientific hypotheses. However, the emergence of machine learning (ML) algorithms has opened new opportunities for exploring variable relationships beyond linear constraints.
Po‐Hsien Huang
wiley   +1 more source

Virtual cell construction for artificial intelligence‐driven drug discovery

open access: yesBritish Journal of Pharmacology, EarlyView.
Cells are the fundamental units through which genetic variation and pharmacological perturbations influence disease processes and therapeutic responses. However, cellular responses to intervention are strongly shaped by biological context, creating a central challenge for drug discovery: predicting how specific perturbations reshape cellular systems ...
Yuran Jia   +5 more
wiley   +1 more source

NePO: Neural Point Octrees for Large‐Scale Novel View Synthesis

open access: yesComputer Graphics Forum, EarlyView.
We introduce Neural Point Octrees (NePOs), a scalable radiance field representation that organises point clouds hierarchically for efficient optimisation and rendering of large scale scenes. NePOs enable level of detail selection, joint refinement of appearance and camera poses, and real‐time rendering of hundreds of millions of points.
Noah Lewis   +3 more
wiley   +1 more source

Self‐supervised Learning of Fine‐to‐Coarse Cuboid Shape Abstraction

open access: yesComputer Graphics Forum, EarlyView.
Abstract The abstraction of 3D objects with simple geometric primitives like cuboids allows us to infer structural information from complex geometry. It is important for 3D shape understanding, structural analysis and geometric modeling. We introduce a novel fine‐to‐coarse self‐supervised learning approach to abstract collections of 3D shapes.
Gregor Kobsik   +6 more
wiley   +1 more source

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