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Found 28 papers, 7 papers with code
Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming
Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle.
On Calibration of Graph Neural Networks for Node Classification
We investigate the calibration of graph neural networks for node classification, study the effect of existing post-processing calibration methods, and analyze the influence of model capacity, graph density, and a new loss function on calibration.
Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction
Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks.
Adaptive Observer for a Class of Systems with Switched Unknown Parameters Using DREM
These responses depend on the unknown states at switching instants (SASI) and constitute an additive disturbance to the parameter estimation, which obstructs parameter convergence to zero.
Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information
In this context, the value of V2X communications for DRL-based platoon controllers is studied with an emphasis on the tradeoff between the gain of including exogenous information in the system state for reducing uncertainty and the performance erosion due to the curse-of-dimensionality.
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training
The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization.
On the evaluation of (meta-)solver approaches
Meta-solver approaches exploits a number of individual solvers to potentially build a better solver.
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection
The network intrusion detection task is challenging because of the imbalanced and unlabeled nature of the dataset it operates on.
From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba
Except for introducing future knowledge for prediction, we propose Aliformer based on the bidirectional Transformer, which can utilize the historical information, current factor, and future knowledge to predict future sales.
Fine-Grained Element Identification in Complaint Text of Internet Fraud
Existing system dealing with online complaint provides a final decision without explanations.
Multi-Level Graph Contrastive Learning
In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs.
Prior Based Human Completion
One is a structure prior, it uses a human parsing map to represent the human body structure.
Photometric space object classification via deep learning algorithms
Accurate time transfer by time of flight measurements via diffuse reflections on passive orbiting space debris targets requires a selection of suitable objects out of a large catalogue of debris items.
Model Reference Adaptive Control of Piecewise Affine Systems with State Tracking Performance Guarantees
We also prove that the Lyapunov function is non-increasing even at the switching instants and thus does not impose extra dwell time constraints.
Comparison of prismatic cohomology and derived de Rham cohomology
We establish a comparison isomorphism between prismatic cohomology and derived de Rham cohomology respecting various structures, such as their Frobenius actions and filtrations.
Algebraic Geometry Number Theory 14F30, 11F80
Tucker decomposition-based Temporal Knowledge Graph Completion
Developing the model for temporal knowledge graphs completion is an increasingly important task.
Self-supervised Graph Representation Learning via Bootstrapping
In addition, we summarize three kinds of augmentation methods for graph-structured data and apply them to the DGB.
ECG Beats Fast Classification Base on Sparse Dictionaries
In VQ method, a set of dictionaries corresponding to segments of ECG beats is trained, and VQ codes are used to represent each heartbeat.
sunny-as2: Enhancing SUNNY for Algorithm Selection
A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems.
Neighborhood-based Pooling for Population-level Label Distribution Learning
Supervised machine learning often requires human-annotated data.
Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer
Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day.
Proximal Policy Optimization with Mixed Distributed Training
Instability and slowness are two main problems in deep reinforcement learning.
Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop
We present the Twitter Job/Employment Corpus, a collection of tweets annotated by a humans-in-the-loop supervised learning framework that integrates crowdsourcing contributions and expertise on the local community and employment environment.
Segmentation of histological images and fibrosis identification with a convolutional neural network
Segmentation of histological images is one of the most crucial tasks for many biomedical analyses including quantification of certain tissue type.
Learning from various labeling strategies for suicide-related messages on social media: An experimental study
Suicide is an important but often misunderstood problem, one that researchers are now seeking to better understand through social media.
Learning to Identify Sentence Parallelism in Student Essays
Parallelism is an important rhetorical device.
