Search Results for author:
Found 9 papers, 1 papers with code
An Attention-driven Two-stage Clustering Method for Unsupervised Person Re-Identification
By iteratively learning with the two strategies, the attentive regions are gradually shifted from the background to the foreground and the features become more discriminative.
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm.
Initial Structure to Relaxed Energy (IS2RE), Direct
Property Prediction
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.
Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction
Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties.
Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways
In this study, we build a computational model to elucidate the computational advantages associated with the interactions between two pathways.
Unsupervised Few-shot Learning via Self-supervised Training
The proposed model consists of two alternate processes, progressive clustering and episodic training.
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
Experimental data has revealed that in addition to feedforward connections, there exist abundant feedback connections in a neural pathway.
Unsupervised Few Shot Learning via Self-supervised Training
Using the benchmark dataset Omniglot, we show that our model outperforms other unsupervised few-shot learning methods to a large extend and approaches to the performances of supervised methods.
Spatiotemporal Information Processing with a Reservoir Decision-making Network
Spatiotemporal information processing is fundamental to brain functions.
