Search Results for author:
Found 8 papers, 4 papers with code
Learning from Sparse Offline Datasets via Conservative Density Estimation
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.
Resampling Stochastic Gradient Descent Cheaply for Efficient Uncertainty Quantification
Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization.
Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model
Then, two competing student models trained by normal and abnormal graphs respectively fit graph representations of the teacher model in terms of node-level and graph-level representation perspectives.
Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design
More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously.
Multi-representations Space Separation based Graph-level Anomaly-aware Detection
The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies.
A serial dual-channel library occupancy detection system based on Faster RCNN
The phenomenon of seat occupancy in university libraries is a prevalent issue.
Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points
A novel approach is then proposed to construct the graph of the input data from the learned graph of a small number of vertexes with some preferred properties.
Hierarchical Neural Architecture Search via Operator Clustering
Recently, the efficiency of automatic neural architecture design has been significantly improved by gradient-based search methods such as DARTS.
