Results 81 to 90 of about 31,077 (302)
Neural Network Pruning by Cooperative Coevolution
, 2022 Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or using auxiliary modules to ...
Shang, Haopu +3 more
core
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source
Influence Maximization Algorithm for Dynamic Social Networks Based on Linear Threshold Model
工程科学与技术, 2019 In order to solve the influence maximization problem in evolving social network, a dynamic influence maximization algorithm based on the linear threshold model was proposed in this paper.
Jinghua ZHU +3 more
doaj
Effectiveness of Different Pruning Methods on Enhancing Vegetative Growth and Productivity of Jojoba Shrubs [PDF]
Horticulture Research JournalA field experiment was conducted during 2023 and 2024 seasons on jojoba shrubs (Simmondsia chinensis (Link) Schneider), grown at a commercial jojoba plantation (private farm, Cairo Alexandria desert road, Egypt) to evaluate the impact of different ...
Nahla. E. A. Shohba +2 more
doaj +1 more source
Pruning Algorithms for Multi-model Adversary Search
, 1998 The Multi-model search framework generalizes minimax to allow exploitation of recursive opponent models. In this work we consider adding pruning to the multi-model search.
Shaul Markovitch +3 more
core +1 more source
Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents
Advanced Intelligent Discovery, EarlyView.This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
local lemma: a new strategy of pruning in sat solvers
, 2010 ACM Special Interest Group on Applied Computing (SIGAPP); Hes.so; icare; CUSSTThis paper proposes a search tree pruning strategy for SAT solving. It is called Local Lemma, because it generates lemmas from explored subtrees and these lemmas are valid only
Liu Sheng +3 more
core
Metaheuristic approaches for optimal broadcasting design in metropolitan MANETs [PDF]
, 2007 11th International Conference on Computer Aided Systems Theory. Las Palmas de Gran Canaria, Spain, February 12-16, 2007Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any
Luque del Arco Calderón, Cristobal +13 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source