Search Results for author:
Found 8 papers, 2 papers with code
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR
In recent years, emerging research on mobile sensing has led to novel scenarios that enhance daily life for humans, but dynamic usage conditions often result in performance degradation when systems are deployed in real-world settings.
Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge
Remote photoplethysmography (rPPG) is a promising technology that captures physiological signals from face videos, with potential applications in medical health, emotional computing, and biosecurity recognition.
On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning
We then propose a new multi-agent based hierarchical deep reinforcement learning (MAHDRL) framework with two tiers to solve the problem efficiently, where the soft actor critic (SAC) policy is designed in tier-1 to determine each UAV's continuous trajectory and binary WET decision over time slots, and the deep-Q learning (DQN) policy is designed in tier-2 to determine each UAV's binary WDC decisions over sub-slots under the given UAV trajectory from tier-1.
Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection
While large language models (LLMs) such as ChatGPT and PaLM have demonstrated remarkable performance in various language understanding and generation tasks, their capabilities in complex reasoning and intricate knowledge utilization still fall short of human-level proficiency.
Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI
With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc.
MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis
Recent studies show that deep learning models achieve good performance on medical imaging tasks such as diagnosis prediction.
Predicting ratings in multi-criteria recommender systems via a collective factor model
In a multi-criteria recommender system, users are allowed to give an overall rating to an item and provide a score on each of its attribute.
Ranked #1 on
Recommendation Systems
on BeerAdvocate
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning
Deep reinforcement learning has been successfully used in many dynamic decision making domains, especially those with very large state spaces.
