Results 191 to 200 of about 438 (214)

Testing standard basis sets for direct ionizations: H⁺ + H at E_Lab = 0.1-100 keV. [PDF]

J Comput Chem
Kim H, Morales JA.
europepmc   +1 more source

A two-stage differential evolution algorithm for neural ensemble architecture search. [PDF]

Sci Rep
Zhao H, Cheng X, Ding W, Li H, Gong S, Xing X.   +5 more
europepmc   +1 more source

QCMaquis 4.0: Multipurpose Electronic, Vibrational, and Vibronic Structure and Dynamics Calculations with the Density Matrix Renormalization Group. [PDF]

J Phys Chem A
Szenes K, Glaser N, Erakovic M, Barandun V, Mörchen M, Feldmann R, Battaglia S, Baiardi A, Reiher M.   +8 more
europepmc   +1 more source

De Novo Reconstruction of 3D Human Facial Images from DNA Sequence. [PDF]

Adv Sci (Weinh)
Jiao M, Li J, Zhong B, Du S, Li S, Zhang M, Zhang Q, Liang Z, Liu F, Zuo C, Wang S, Chen L.   +11 more
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Dynamic Determinantal Point Processes

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
The determinantal point process (DPP) has been receiving increasing attention in machine learning as a generative model of subsets consisting of relevant and diverse items. Recently, there has been a significant progress in developing efficient algorithms for learning the kernel matrix that characterizes a DPP.
Takayuki Osogami, Rudy Raymond, Akshay Goel, Tomoyuki Shirai, Takanori Maehara   +4 more
openaire   +1 more source

Tensorized Determinantal Point Processes for Recommendation

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Interest in determinantal point processes (DPPs) is increasing in machine learning due to their ability to provide an elegant parametric model over combinatorial sets. In particular, the number of required parameters in a DPP grows only quadratically with the size of the ground set (e.g., item catalog), while the number of possible sets of items grows ...
Romain Warlop, Jérémie Mary, Mike Gartrell   +2 more
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Tweet Timeline Generation with Determinantal Point Processes

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
The task of tweet timeline generation (TTG) aims at selecting a small set of representative tweets to generate a meaningful timeline and providing enough coverage for a given topical query. This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and ...
Jin-ge Yao, Feifan Fan, Wayne Xin Zhao, Xiaojun Wan 0001, Edward Y. Chang, Jianguo Xiao   +5 more
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Bayesian Low-Rank Determinantal Point Processes

Proceedings of the 10th ACM Conference on Recommender Systems, 2016
Determinantal point processes (DPPs) are an emerging model for encoding probabilities over subsets, such as shopping baskets, selected from a ground set, such as an item catalog. They have recently proved to be appealing models for a number of machine learning tasks, including product recommendation.
Mike Gartrell, Ulrich Paquet, Noam Koenigstein   +2 more
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Determinantal point processes conditioned on randomly incomplete configurations

Annales De L'institut Henri Poincare (B) Probability and Statistics, 2023
Tom Claeys
exaly  

A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes

IEEE Transactions on Evolutionary Computation, 2021
Huanhuan Chen, Tengfei Li, Jinlong Li
exaly