Search Results for author:
Found 7 papers, 1 papers with code
An Automated Vulnerability Detection Framework for Smart Contracts
With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology.
Fairness-Aware Online Meta-learning
To overcome such issues and bridge the gap, in this paper for the first time we proposed a novel online meta-learning algorithm, namely FFML, which is under the setting of unfairness prevention.
Imbalanced Adversarial Training with Reweighting
Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks.
Towards Self-Adaptive Metric Learning On the Fly
In this paper, we present a new online metric learning framework that attempts to tackle the challenge by learning an ANN-based metric with adaptive model complexity from a stream of constraints.
Progressive One-shot Human Parsing
In this paper, we devise a novel Progressive One-shot Parsing network (POPNet) to address two critical challenges , i. e., testing bias and small sizes.
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation
In this paper, we investigate the effectiveness of data augmentation techniques in mitigating backdoor attacks and enhancing DL models' robustness.
ChainNet: Learning on Blockchain Graphs with Topological Features
With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction.
