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Found 19 papers, 5 papers with code
Disentangling Uncertainties by Learning Compressed Data Representation
Disentangling aleatoric uncertainty (the inherent randomness of the system) from epistemic uncertainty (the lack of data) is crucial for downstream tasks such as risk-aware control and reinforcement learning, efficient exploration, and robust policy transfer.
GWRF: A Generalizable Wireless Radiance Field for Wireless Signal Propagation Modeling
We present Generalizable Wireless Radiance Fields (GWRF), a framework for modeling wireless signal propagation at arbitrary 3D transmitter and receiver positions.
See Behind Walls in Real-time Using Aerial Drones and Augmented Reality
This work presents ARD2, a framework that enables real-time through-wall surveillance using two aerial drones and an augmented reality (AR) device.
Magnetic Distortion Resistant Orientation Estimation
MDR builds a database to record magnetic directions at different locations and uses it to correct orientation estimates affected by magnetic distortion.
Tri-Cam: Practical Eye Gaze Tracking via Camera Network
As human eyes serve as conduits of rich information, unveiling emotions, intentions, and even aspects of an individual's health and overall well-being, gaze tracking also enables various human-computer interaction applications, as well as insights in psychological and medical research.
See Where You Read with Eye Gaze Tracking and Large Language Model
Furthermore, real field tests with 18 volunteers demonstrated the system's effectiveness in tracking and highlighting read paragraphs, improving reading efficiency, and enhancing user experience.
Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base
This paper introduces Golden-Retriever, designed to efficiently navigate vast industrial knowledge bases, overcoming challenges in traditional LLM fine-tuning and RAG frameworks with domain-specific jargon and context interpretation.
A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control
This paper presents CLUE, an MBRL system for HVAC control in buildings.
MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge
To that end, we analyze four-year in-field datasets, which reveal the multi-periodicity feature of the soil oxygen level trends and the opportunity to use external weather forecasts and flooding proposals as exogenous clues for soil oxygen prediction.
Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control
We found that the high dimensionality of the thermal dynamics model input hinders the efficiency of policy extraction.
Reward Bound for Behavioral Guarantee of Model-based Planning Agents
Recent years have seen an emerging interest in the trustworthiness of machine learning-based agents in the wild, especially in robotics, to provide safety assurance for the industry.
CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation
We develop the CloudEval-YAML benchmark with practicality in mind: the dataset consists of hand-written problems with unit tests targeting practical scenarios.
Ranked #1 on
Benchmarking
on CloudEval-YAML
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks.
Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field
The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss.
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning
In this paper, we conduct a set of experiments to analyze the limitations of current MBRL-based HVAC control methods, in terms of model uncertainty and controller effectiveness.
Exploring Deep Reinforcement Learning for Holistic Smart Building Control
The DRL architecture includes a novel reward function that allows the framework to explore the tradeoffs between energy use and users' comfort, while at the same time enabling the solution of the high-dimensional control problem due to the interactions of four different building subsystems.
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i. e., early stopping (ES) strategy and reward-level interactive (RI) strategy.
Retrieving Similar Trajectories from Cellular Data at City Scale
Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis.
Performance Analysis and Characterization of Training Deep Learning Models on Mobile Devices
In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models.
