Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks152 from public data
- Convolutional Sequence to Sequence Model for Human Dynamics376
This work presents a novel approach to human motion modeling based on convolutional neural networks (CNN), which is able to capture both invariant and dynamic information of human motion, which results in more accurate predictions.
- 371
- RadixPuerariae: An overview of Its Chemistry, Pharmacology, Pharmacokinetics, and Clinical Use217
The current review is proposed to cover its traditional applications, pharmacological activities, pharmacokinetics, clinical efficacy, and potential herb–drug interactions aiming to fill in the information gaps of this herb for frontline practitioners.
- Improved key-rate bounds for practical decoy-state quantum-key-distribution systems148
A tighter bound is developed for the decoy-state method, which yields a smaller failure probability and results in a higher key rate and increases the maximum distance over which secure key exchange is possible.
- Learning Deep Gradient Descent Optimization for Image Deconvolution120
A recurrent gradient descent network (RGDN) is proposed by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme and learns an implicit image prior and a universal update rule through recursive supervision.
- Matching user accounts based on user generated content across social networks86
This work first formulate the problem of user identification based on UGCs and then proposes a UGC-based user identification model, and presents the possibility of matching user accounts with high accessible online data.
- 85
- 75
- 62
- Factor Graph Neural Networks55
This work derives an efficient approximate Sum-Product loopy belief propagation inference algorithm for discrete higher-order PGMs, and neuralizes the novel message passing scheme into a Factor Graph Neural Network (FGNN) module by allowing richer representations of the message update rules, which facilitates both efficient inference and powerful end-to-end learning.
- 51
- 50
- 49
- NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results45
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022, and the competition set-up, datasets, proposed methods and their results.
- 43
- Random Sampling SVM Based Soft Query Expansion for Image Retrieval43
The experiments show that the wrinkle generated by this method not only has the advantage of verisimilitude as a photograph, but also has the feature of natural ripple which is deeper in middle than in both sides.
- A bidirectional graph neural network for traveling salesman problems on arbitrary symmetric graphs40
A bidirectional graph neural network (BGNN) for the arbitrary symmetric TSP that learns to produce the next city to visit sequentially by imitation learning and is able to be combined with informed search to further improve performance.
- 39
- Pharmacokinetic and pharmacodynamic interaction of Danshen–Gegen extract with warfarin and aspirin38
It is indicated that co-administration of DG with warfarin/aspirin can cause significant pharmacokinetic and pharmacodynamic herb-drug interactions in rat, and danshensu were not significantly affected by aspirin.
- 37
- 36
- Applications of Zeroing Neural Networks: A Survey32
This review article explores the practical applications of ZNN across various domains in the past two decades, specifically focusing on robot manipulator path tracking, motion planning, and chaotic systems.
- 28
- Identifiable Latent Polynomial Causal Models Through the Lens of Change27
The scope of latent causal models is extended to involve nonlinear causal relationships, represented by polynomial models, and general noise distributions conforming to the exponential family, and a novel empirical estimation method is proposed, grounded in the theoretical finding, that enables learning consistent latent causal representations.
- A new swarm-SVM-based fault diagnosis approach for switched current circuit by using kurtosis and entropy as a preprocessor27
A particle swarm optimization is proposed to optimize the SVM to diagnose switched current circuits and shows that the method performs well in the part of fault diagnostic accuracy.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.