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Found 12 papers, 2 papers with code
Uncertainty-oriented Order Learning for Facial Beauty Prediction
To address these issues, we propose a new Uncertainty-oriented Order Learning (UOL), where the order learning addresses the inconsistency of FB standards by learning the FB order relations among face images rather than a mapping, and the uncertainty modeling represents the inconsistency in human cognition.
Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing
Assistive drawing aims to facilitate the creative process by providing intelligent guidance to artists.
Sketch2Manga: Shaded Manga Screening from Sketch with Diffusion Models
Unfortunately, there is no existing method that tailors for automatic manga screening, probably due to the difficulty of generating high-quality shaded high-frequency screentones.
Instance-guided Cartoon Editing with a Large-scale Dataset
Cartoon editing, appreciated by both professional illustrators and hobbyists, allows extensive creative freedom and the development of original narratives within the cartoon domain.
Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater
Robots play a critical role as the physical agent of human operators in exploring the ocean.
Intelligence-Endogenous Management Platform for Computing and Network Convergence
Therefore, in this article, we present the concept of an intelligence-endogenous management platform for CNCs called \emph{CNC brain} based on artificial intelligence technologies.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
Hard instance learning for quantum adiabatic prime factorization
Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography.
Quantum Adiabatic Doping for Atomic Fermi-Hubbard Quantum Simulations
Its feasibility has been demonstrated with numerical simulations of the adiabatic preparation for certain incommensurate particle-doping fractions, where the major problem to circumvent is the atomic localization in the incommensurate lattice.
Quantum Gases Strongly Correlated Electrons Quantum Physics
Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN
A significant novelty of the proposed RGAN is that it combines the supervised and regressional convolutional neural network (CNN) with the traditional unsupervised GAN, thus overcoming the common technical barrier in the traditional GANs, which cannot generate data associated with given continuous quantitative labels.
Computational Physics Materials Science Applied Physics
Quantum Adiabatic Algorithm Design using Reinforcement Learning
We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-algorithm obtained by our RL approach leads to significant improvement in the resultant success probability.
Deep Learning Bandgaps of Topologically Doped Graphene
Moreover, given ubiquitous existence of topologies in materials, this work will stimulate widespread interests in applying deep learning algorithms to topological design of materials crossing atomic, nano-, meso-, and macro- scales.
Materials Science Computational Physics
