Search Results for author:
Found 7 papers, 2 papers with code
Snow Removal for LiDAR Point Clouds with Spatio-temporal Conditional Random Fields
The proposed approach first constructs the CRF based on k-nearest neighbors with the snow confidence derived from the physical priors of snow, such as intensity and distribution.
Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans
To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models without leakage of data privacy.
Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain
Specifically for malware detection task, (i) we propose a novel user (local) neural network (LNN) which trains on local distribution and (ii) then to assure the model authenticity and quality, we propose a novel smart contract which enable aggregation process over blokchain platform.
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization.
A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation
However, the sampled information from latent space usually becomes useless due to the KL divergence vanishing issue, and the highly abstractive global variables easily dilute the personal features of replier, leading to a non replier-specific response.
Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks
In this paper, we report a robust hierarchical synthesis reconstruction approach, where training data is pre-processed to separate the information on the domains where sampling biases are suspected.
Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression
A major challenge for building statistical models in the big data era is that the available data volume far exceeds the computational capability.
