Search Results for author:
Found 36 papers, 4 papers with code
TripCast: Pre-training of Masked 2D Transformers for Trip Time Series Forecasting
Deep learning and pre-trained models have shown great success in time series forecasting.
Relative-Translation Invariant Wasserstein Distance
We introduce a new family of distances, relative-translation invariant Wasserstein distances ($RW_p$), for measuring the similarity of two probability distributions under distribution shift.
RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment
Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini achieve performance competitively with human experts across various medical benchmarks.
Intra-symbol Differential Amplitude Shift Keying-aided Blind Detector for Ambient Backscatter Communication Systems
Simulation results demonstrate that the proposed blind detector exhibits a significant enhancement in symbol detection performance compared to its traditional counterparts.
DHGS: Decoupled Hybrid Gaussian Splatting for Driving Scene
This paper introduces a novel neural rendering method termed Decoupled Hybrid Gaussian Splatting (DHGS), targeting at promoting the rendering quality of novel view synthesis for static driving scenes.
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
LLMs fine-tuned with this data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval.
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
This survey provides a comprehensive overview of Medical Large Language Models (Med-LLMs), outlining their evolution from general to the medical-specific domain (i. e, Technology and Application), as well as their transformative impact on healthcare (e. g., Trustworthiness and Safety).
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems
Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets.
Reinforcement Learning with Latent State Inference for Autonomous On-ramp Merging under Observation Delay
L3IS shows a 99. 90% success rate in a challenging on-ramp merging case generated from the real US Highway 101 data.
Edge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls
Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources.
Progressive Feature Fusion Network for Enhancing Image Quality Assessment
To capture the subtle differences, a fine-grained network is adopted to acquire multi-scale features.
Two-stage space construction for real-time modeling of distributed parameter systems under sparse sensing
The high-dimensional space construction method is employed to derive continuous spatial basis functions (SBFs).
Fusion-Driven Tree Reconstruction and Fruit Localization: Advancing Precision in Agriculture
Fruit distribution is pivotal in shaping the future of both agriculture and agricultural robotics, paving the way for a streamlined supply chain.
Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems
Numerous industrial thermal processes and fluid processes can be described by distributed parameter systems (DPSs), wherein many process parameters and variables vary in space and time.
Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconfliction
By using DCB to precondition traffic to proper density levels, we show that reinforcement learning can achieve much better performance for tactical safety separation.
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
To this end, we propose MAC-PO, which formulates optimal prioritized experience replay for multi-agent problems as a regret minimization over the sampling weights of transitions.
A Survey on Reinforcement Learning in Aviation Applications
Then we survey the landscape of existing RL-based applications in aviation.
A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems
We present a safety verification framework for design-time and run-time assurance of learning-based components in aviation systems.
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.
Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning
Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM).
Power System Transient Modeling and Simulation using Integrated Circuit
Transient stability analysis (TSA) plays an important role in power system analysis to investigate the stability of power system.
Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance
We demonstrate the effectiveness of the two sub-modules in an open-source air traffic simulator with challenging environment settings.
Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict
Our results show that on commodity GPU hardware we can perform flight plan scheduling against 2000-3000 known flight plans and with server-class hardware the performance can be higher.
Continuous Control for Searching and Planning with a Learned Model
Decision-making agents with planning capabilities have achieved huge success in the challenging domain like Chess, Shogi, and Go.
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance
A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic sector.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
An Efficient Algorithm for Multiple-Pursuer-Multiple-Evader Pursuit/Evasion Game
We present a method for pursuit/evasion that is highly efficient and and scales to large teams of aircraft.
Prioritized Sequence Experience Replay
Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past.
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach
Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments.
Autonomous Airline Revenue Management: A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking
This paper focuses on the latter problem - the seat inventory control problem - casting it as a Markov Decision Process to be able to find the optimal policy.
A PCB Dataset for Defects Detection and Classification
To coupe with the difficulties in the process of inspection and classification of defects in Printed Circuit Board (PCB), other researchers have proposed many methods.
Basis Signal Optimization for N-Continuous OFDM
A novel basis signal optimization method is proposed for reducing the interference in the N-continuous orthogonal frequency division multiplexing (NC-OFDM) system.
AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture
Such a well-defined template is able to support efficient accelerator designs for a broad class of computation kernels, and more importantly, drastically reduce the design space.
Distributed, Parallel, and Cluster Computing Hardware Architecture
Explainable Deterministic MDPs
We present a method for a certain class of Markov Decision Processes (MDPs) that can relate the optimal policy back to one or more reward sources in the environment.
Hierarchical Reinforcement Learning with Deep Nested Agents
Deep hierarchical reinforcement learning has gained a lot of attention in recent years due to its ability to produce state-of-the-art results in challenging environments where non-hierarchical frameworks fail to learn useful policies.
Memoryless Exact Solutions for Deterministic MDPs with Sparse Rewards
The algorithm to solve the MDP does not depend on the size of the state space for either time or memory complexity, and the ability to follow the optimal policy is linear in time and space with the path length of following the optimal policy from the initial state.
Fast Online Exact Solutions for Deterministic MDPs with Sparse Rewards
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision making under uncertainty.
