To solve this issue, there has been a number of work trying to improve model fairness by using an adversarial game in model level.
In the first stage, the surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing the required vehicle propulsion energy under currents, and the line-of-sight (LOS) guidance law is used to generate the yaw angle setpoint that ensures path following.
Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos.
For commercial cloud speech APIs, we propose Occam, a decision-only black-box adversarial attack, where only final decisions are available to the adversary.
To overcome the challenge, we reformulate the problem as a Markov game and propose an energy management algorithm to solve it based on multi-agent discrete actor-critic with rules (MADACR).
To this end, we propose a teacher model fingerprinting attack to infer the origin of a student model, i. e., the teacher model it transfers from.
In deep learning, a typical strategy for transfer learning is to freeze the early layers of a pre-trained model and fine-tune the rest of its layers on the target domain.
While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from neighborhood and generate indistinguishable user representations, which is known as over-smoothing.
To tackle this research gap, we propose a novel duet representation learning framework named \sysname to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-$N$ recommendation, which is composed of two separate sub-models.
We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts.
However, it is a nontrivial task due to the following challenges.
In this paper, we present a unified framework for detecting malicious examples and protecting the inference results of Deep Learning models.
In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building under dynamic pricing with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort.
Graph convolution network (GCN) attracts intensive research interest with broad applications.
This paper proposes a new scheme for privacy-preserving neural network prediction in the outsourced setting, i. e., the server cannot learn the query, (intermediate) results, and the model.
Collaborative learning allows multiple clients to train a joint model without sharing their data with each other.
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks.
We also present an algorithm that can successfully enable attacks against famous cloud-based image services (such as those from Microsoft Azure, Aliyun, Baidu, and Tencent) and cause obvious misclassification effects, even when the details of image processing (such as the exact scaling algorithm and scale dimension parameters) are hidden in the cloud.
High-frequency heart rate variability (HRV) has identified parasympathetic nervous system alterations in autism spectrum disorder (ASD).
Quantitative Methods Neurons and Cognition
This paper presents a novel user authentication system through wrist-worn devices by analyzing the interaction behavior with users, which is both accurate and efficient for future usage.