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Found 14 papers, 4 papers with code
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms.
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy
To fast solve the non-smooth loss under a given privacy budget, we develop a Fast Robust And Privacy-Preserving Estimation (FRAPPE) algorithm for least absolute deviation regression.
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving
Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception.
Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial
Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4.
Efficient Quality-Diversity Optimization through Diverse Quality Species
This can be rectified by Quality-Diversity (QD) algorithms, where a population of high-quality and diverse solutions to a problem is preferred.
Channel Modeling for UAV-to-Ground Communications with Posture Variation and Fuselage Scattering Effect
Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a pivotal role for reliable communications between UAV and ground terminal.
Learning-Based Data Storage [Vision] (Technical Report)
As a case study, we use DNNs as the engine in the LMU, and study the data capacity and accuracy of the DNN-based data storage.
Edge Semantic Cognitive Intelligence for 6G Networks: Novel Theoretical Models, Enabling Framework, and Typical Applications
Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated.
A Generic Graph Sparsification Framework using Deep Reinforcement Learning
The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and decision-making of these graphs.
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features
In this work, we propose a unified framework to tackle the non-iid issues for internal and external clients together.
One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal
Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor.
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network
A potential approach to this problem is to design an end-to-end method by using a dual generative adversarial network (DualGAN) without dimension reduction of passing information, but it cannot realize one-to-one signal-to-signal translation (see Fig. 1 (a) and (b)).
LRN: Limitless Routing Networks for Effective Multi-task Learning
Multi-task learning (MTL) is a field involved with learning multiple tasks simultaneously typically through using shared model parameters.
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.
