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Found 14 papers, 2 papers with code
An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music Recommendation
In order to improve the quality of AI music generation and further guide computer music production, synthesis, recommendation and other tasks, we use Birkhoff's aesthetic measure to design a aesthetic model, objectively measuring the aesthetic beauty of music, and form a recommendation list according to the aesthetic feeling of music.
Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models
We train DDPMs to estimate the ISRF using synthetic three-band dust emission.
An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance
In order to guide the generation task of AI music performance, and to improve the performance effect of human performers, this paper uses Birkhoff's aesthetic measure to propose a method of objective measurement of beauty.
Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds
We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps.
An Order-Complexity Model for Aesthetic Quality Assessment of Symbolic Homophony Music Scores
Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored.
Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3D Ultrasound Imaging System
A total of 127 3D carotid artery scans were acquired using a portable 3D US system which consisted of a handheld US scanner and an electromagnetic tracking system.
Generalizing LTL Instructions via Future Dependent Options
In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations.
VulCNN: An Image-inspired Scalable Vulnerability Detection System
Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN has the ability to detect large-scale vulnerability.
On the Steady-State Behavior of Finite-Control-Set MPC with an Application to High-Precision Power Amplifiers
To improve the steady-state behavior of FCS-MPC, in this paper we design a cost function that penalizes the tracking error with respect to a state and input steady-state limit cycle.
Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming
In this work, we propose a new hierarchical framework via symbolic RL, leveraging a symbolic transition model to improve the data-efficiency and introduce the interpretability for learned policy.
Hierarchical Reinforcement Learning
Inductive logic programming
+2
Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method
In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate the policy network of actor-critic RL with HMC, which is termed as {\it Hamiltonian Policy}.
Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback
Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning.
Deep Reinforcement Learning with Implicit Human Feedback
Building atop the baseline, we then make the following novel contributions in our work: (i) We argue that the definition of error-potentials is generalizable across different environments; specifically we show that error-potentials of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials.
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler
State space models (SSM) have been widely applied for the analysis and visualization of large sequential datasets.
