Search Results for author:
Found 9 papers, 4 papers with code
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise.
SIMGA: A Simple and Effective Heterophilous Graph Neural Network with Efficient Global Aggregation
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i. e. neighboring nodes are dissimilar, due to their local and uniform aggregation.
Detecting Multivariate Time Series Anomalies with Zero Known Label
Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required.
LEBP -- Language Expectation & Binding Policy: A Two-Stream Framework for Embodied Vision-and-Language Interaction Task Learning Agents
People always desire an embodied agent that can perform a task by understanding language instruction.
NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks
Machine Learning (ML) techniques are increasingly adopted to tackle ever-evolving high-profile network attacks, including DDoS, botnet, and ransomware, due to their unique ability to extract complex patterns hidden in data streams.
Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search
The NAR explores the quality tiers of the search space globally and classifies each individual to the tier they belong to according to its global ranking.
Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression
Specifically, in LGC, local gradients from a device is coded into several layers and each layer is sent to the FL server along a different channel.
Fairness-aware Outlier Ensemble
Meanwhile, we propose a post-processing framework to tune the original ensemble results through a stacking process so that we can achieve a trade off between fairness and detection performance.
Image Matters: Visually modeling user behaviors using Advanced Model Server
So we propose to model user preference jointly with user behavior ID features and behavior images.
