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Found 7 papers, 1 papers with code
Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding
In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information in recent years.
Case-Aware Adversarial Training
Specifically, the intuition stems from the fact that a very limited part of informative samples can contribute to most of model performance.
Enhance transferability of adversarial examples with model architecture
Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model.
Object Tracking by Least Spatiotemporal Searches
This paper proposes a strategy named IHMs (Intermediate Searching at Heuristic Moments): each step we figure out which moment is the best to search according to a heuristic indicator, then at that moment search locations one by one in descending order of predicted appearing probabilities, until a search hits; iterate this step until we get the object's current location.
HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions
To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.
Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings
Recently, several knowledge graph embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets.
A novel particle swarm optimizer with multi-stage transformation and genetic operation for VLSI routing
Firstly, in order to be able to handle two types of SMT problems at the same time, an effective edge-vertex encoding strategy is proposed.
