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Found 26 papers, 10 papers with code
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
To address these two pain points, we propose QuantumSEA, an in-time sparse exploration for noise-adaptive quantum circuits, aiming to achieve two key objectives: (1) implicit circuits capacity during training - by dynamically exploring the circuit's sparse connectivity and sticking a fixed small number of quantum gates throughout the training which satisfies the coherence time and enjoy light noises, enabling feasible executions on real quantum devices; (2) noise robustness - by jointly optimizing the topology and parameters of quantum circuits under real device noise models.
Spatiotemporal-Linear: Towards Universal Multivariate Time Series Forecasting
These extra routes offer a more robust and refined regression to the data, particularly when the amount of observation is limited and the capacity of simple linear layers to capture dependencies declines.
RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training
Arbitrary state preparation algorithms can be broadly categorized into arithmetic decomposition (AD) and variational quantum state preparation (VQSP).
Leveraging Multi-stream Information Fusion for Trajectory Prediction in Low-illumination Scenarios: A Multi-channel Graph Convolutional Approach
The optical flow channel is applied to capture the pattern of relative motion between adjacent camera frames and modelled by Spatial-Temporal Graph Convolutional Network (ST-GCN).
QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency.
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery
But in uncertain environments, they are not reliable, so learning-based methods are proposed, especially reinforcement learning (RL) methods.
Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways
Real-time safety systems are crucial components of intelligent vehicles.
QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naive parameter shift have low fidelity and thus degrading the training accuracy.
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles
Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work.
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments
Several GRL approaches are summarized and implemented in the proposed framework.
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
Furthermore, to improve the robustness against noise, we propose noise injection to the training process by inserting quantum error gates to PQC according to realistic noise models of quantum hardware.
Towards Efficient On-Chip Training of Quantum Neural Networks
The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks.
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection
Federated learning (FL) is a distributed machine learning paradigm that allows clients to collaboratively train a model over their own local data.
Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot
Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility.
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers, QuantumNAS significantly outperforms baselines.
Energy Management Strategy for Unmanned Tracked Vehicles Based on Local Speed Planning
Finally, based on the prediction results, the model predictive control algorithm is used to realize the real-time optimization of energy management.
Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook
Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy.
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks
Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years.
High-precision target positioning system for unmanned vehicles based on binocular vision
In order to verify the effect of the system, this paper collects the accuracy and calculation time of the output results of the cylinder in different poses.
Stochastic Graph Recurrent Neural Network
In addition, to alleviate KL-vanishing problem in SGRNN, a simple and interpretable structure is proposed based on the lower bound of KL-divergence.
Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis
Accurately modelling the driver behavior at the intersection is essential for intelligent transportation systems (ITS).
A Survey on Sensor Technologies for Unmanned Ground Vehicles
Unmanned ground vehicles have a huge development potential in both civilian and military fields, and have become the focus of research in various countries.
From perception to control: an autonomous driving system for a formula student driverless car
This paper introduces the autonomous system of the "Smart Shark II" which won the Formula Student Autonomous China (FSAC) Competition in 2018.
Choosing Transfer Languages for Cross-Lingual Learning
Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages.
Towards a General-Purpose Linguistic Annotation Backend
In this extended abstract, we describe the beginnings of a new project that will attempt to ease this language documentation process through the use of natural language processing (NLP) technology.
Learning and Generalizing Motion Primitives from Driving Data for Path-Tracking Applications
Therefore, the goal of this paper is to generate the prediction results of lateral commands with confidence regions according to the reference based on the learned motion primitives.
