Results 41 to 50 of about 3,865,632 (281)
Remote Sensing, 2023 The reconstruction of 3D shapes from a single view has been a longstanding challenge. Previous methods have primarily focused on learning either geometric features that depict overall shape contours but are insufficient for occluded regions, local ...
Guoqing Gao +5 more
doaj +1 more source
Learning with structured sparsity
Proceedings of the 26th Annual International Conference on Machine Learning, 2009 This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea that has become popular in recent years.
Huang, Junzhou +2 more
openaire +2 more sources
Atomistic structure learning [PDF]
The Journal of Chemical Physics, 2019 One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D compounds and layered structures atom by atom.
J��rgensen, Mathias S. +6 more
openaire +5 more sources
Exploiting Cognitive Structure for Adaptive Learning
, 2019 Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner.
Chang H.-S. +8 more
core +1 more source
An Information Criterion for Inferring Coupling of Distributed Dynamical Systems
Frontiers in Robotics and AI, 2016 The behaviour of many real-world phenomena can be modelled by nonlinear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model by a set of coupled maps as a ...
Oliver Michael Cliff +3 more
doaj +1 more source
Structured learning modulo theories
Artificial Intelligence, 2017 Modelling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hybrid domains is a particularly daunting task. The ability to model this kind of domains is crucial in "learning to design" tasks, that is, learning applications where the goal
Teso, Stefano +2 more
openaire +4 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Although not always achieved, complete chemotherapy‐induced nausea and vomiting (CINV) control is the conventional goal of CINV prophylaxis. In this two‐center, mixed‐methods study, we sought to understand the preferences of adolescent patients and family caregivers for CINV control endpoints.
Haley Newman +8 more
wiley +1 more source
Knowledge graph construction with structure and parameter learning for indoor scene design
Computational Visual Media, 2018 We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor
Yuan Liang +4 more
doaj +1 more source
Structure learning of antiferromagnetic Ising models [PDF]
, 2014 In this paper we investigate the computational complexity of learning the graph structure underlying a discrete undirected graphical model from i.i.d. samples. We first observe that the notoriously difficult problem of learning parities with noise can be
Bresler, Guy +2 more
core +1 more source
Structure Learning in Nested Effects Models
, 2008 Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g. the effects showing in gene
Markowetz, Florian, Tresch, Achim
core +3 more sources