Search Results for author:
Found 12 papers, 3 papers with code
Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey
Although the applications of artificial intelligence especially deep learning had greatly improved various aspects of intelligent manufacturing, they still face challenges for wide employment due to the poor generalization ability, difficulties to establish high-quality training datasets, and unsatisfactory performance of deep learning methods.
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings.
On the Value of Myopic Behavior in Policy Reuse
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
CUR Transformer: A Convolutional Unbiased Regional Transformer for Image Denoising
Based on the prior that, for each pixel, its similar pixels are usually spatially close, our insights are that (1) we partition the image into non-overlapped windows and perform regional self-attention to reduce the search range of each pixel, and (2) we encourage pixels across different windows to communicate with each other.
Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation
While Reinforcement Learning can achieve impressive results for complex tasks, the learned policies are generally prone to fail in downstream tasks with even minor model mismatch or unexpected perturbations.
Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning
Our key insight is that dynamic systems with different parameters provide different levels of difficulty for the policy, and the difficulty of behaving well in a system is constantly changing due to the evolution of the policy.
Neural Topic Modeling with Deep Mutual Information Estimation
NTM-DMIE is a neural network method for topic learning which maximizes the mutual information between the input documents and their latent topic representation.
Evolutionary Action Selection for Gradient-based Policy Learning
Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) have recently been integrated to take the advantage of the both methods for better exploration and exploitation. The evolutionary part in these hybrid methods maintains a population of policy networks. However, existing methods focus on optimizing the parameters of policy network, which is usually high-dimensional and tricky for EA. In this paper, we shift the target of evolution from high-dimensional parameter space to low-dimensional action space. We propose Evolutionary Action Selection-Twin Delayed Deep Deterministic Policy Gradient (EAS-TD3), a novel hybrid method of EA and DRL. In EAS, we focus on optimizing the action chosen by the policy network and attempt to obtain high-quality actions to promote policy learning through an evolutionary algorithm.
Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks
In this paper, we propose a novel generation model based on Topic-aware Pointer-Generator Networks (TPGN), which can utilize the topic information hidden in the articles to guide the generation of pertinent and diversified comments.
Attention-Mechanism-based Tracking Method for Intelligent Internet of Vehicles
Aiming at the problem that the traditional convolutional neural network is vulnerable to background interference, this paper proposes vehicle tracking method based on human attention mechanism for self-selection of deep features with an inter-channel fully connected layer.
Formulating Semantics of Probabilistic Argumentation by Characterizing Subgraphs: Theory and Empirical Results
The results show that our approach not only dramatically decreases the time for computing p(E^\sigma), but also has an attractive property, which is contrary to that of existing approaches: the denser the edges of a PrAG are or the bigger the size of a given extension E is, the more efficient our approach computes p(E^\sigma).
