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Found 26 papers, 7 papers with code
Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback
Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems.
Modeling Link-level Road Traffic Resilience to Extreme Weather Events Using Crowdsourced Data
Climate changes lead to more frequent and intense weather events, posing escalating risks to road traffic.
An Active Noise Control System Based on Soundfield Interpolation Using a Physics-informed Neural Network
An ANC system is designed to take advantage of the interpolated signal to reduce noise signal within the ROI.
Leveraging Contextual Counterfactuals Toward Belief Calibration
Beliefs and values are increasingly being incorporated into our AI systems through alignment processes, such as carefully curating data collection principles or regularizing the loss function used for training.
Substructure Aware Graph Neural Networks
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.
Ranked #7 on
Graph Regression
on ZINC-500k
Research and Optimization of Heliostat Field Layout Based on Mixed Strategy Whale Optimization Algorithm
For the above problems, the new heliostatic field layout derived based on the idea of radiation grid layout and the no-occlusion principle is studied。At the same time, the detailed calculation process of the shadow occlusion efficiency is given in the process of establishing the optical efficiency model, and then the key parameters in the mirror field layout are optimized in combination with the intelligent optimization algorithm.
Leveraging Initial Hints for Free in Stochastic Linear Bandits
We study the setting of optimizing with bandit feedback with additional prior knowledge provided to the learner in the form of an initial hint of the optimal action.
Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images
Even if this type of segmentation-free approaches have been boosted with deep learning, it is not yet clear how well direct approach can compare to segmentation approaches, which are expected to be still more accurate.
The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement
Modern smartphones can continuously stream multi-megapixel RGB images at 60Hz, synchronized with high-quality 3D pose information and low-resolution LiDAR-driven depth estimates.
Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification
The method employs the graph structure to characterize topological relationships and the graph convolutional network (GCN) that is good at processing for crossscene HSI classification.
Time Series Anomaly Detection for Smart Grids: A Survey
With the rapid increase in the integration of renewable energy generation and the wide adoption of various electric appliances, power grids are now faced with more and more challenges.
Roof Damage Assessment from Automated 3D Building Models
Since technicians are drawing these polygons manually with digitizers, depending on aerial photos, not all building polygons are precisely orthogonal.
Locally Linear Region Knowledge Distillation
To the end, the student is able to better capture the local shape of the teacher function and thus achieves a better performance.
Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks
It is well observed that in deep learning and computer vision literature, visual data are always represented in a manually designed coding scheme (eg., RGB images are represented as integers ranging from 0 to 255 for each channel) when they are input to an end-to-end deep neural network (DNN) for any learning task.
GraphFederator: Federated Visual Analysis for Multi-party Graphs
This paper presents GraphFederator, a novel approach to construct joint representations of multi-party graphs and supports privacy-preserving visual analysis of graphs.
Human-Computer Interaction Cryptography and Security Graphics
DASC: Towards A Road Damage-Aware Social-Media-Driven Car Sensing Framework for Disaster Response Applications
iii) How to efficiently guide the cars to the event locations with little prior knowledge of the road damage caused by the disaster, while also handling the dynamics of the physical world and social media?
A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images
Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection.
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training
In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC).
Multi-task Temporal Convolutional Network for Predicting Water Quality Sensor
Also, the changing patterns of several water quality variables can only be revealed by processing long term historical observations, which is not well supported by conventional data-driven models.
A Joint Learning Framework With BERT for Spoken Language Understanding
In this paper, we propose a novel encoder-decoder framework based multi-task learning model, which conducts joint training for intent classification and slot filling tasks.
Data-Free Point Cloud Network for 3D Face Recognition
To ease the inconsistent distribution between model data and real faces, different point sampling methods are used in train and test phase.
Deep Reinforcement Learning for Personalized Search Story Recommendation
In recent years, \emph{search story}, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, news feed platforms and web and image search platforms.
Zero-Shot Learning via Latent Space Encoding
Instead of requiring a projection function to transfer information across different modalities like most previous work, LSE per- forms the interactions of different modalities via a feature aware latent space, which is learned in an implicit way.
Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing.
Machine Learning Based Detection of Clickbait Posts in Social Media
In this paper, we make use of a dataset from the clickbait challenge 2017 (clickbait-challenge. com) comprising of over 21, 000 headlines/titles, each of which is annotated by at least five judgments from crowdsourcing on how clickbait it is.
Boosted Zero-Shot Learning with Semantic Correlation Regularization
For adaptable knowledge transfer, we devise a Semantic Correlation Regularization (SCR) approach to regularize the boosted model to be consistent with the inter-class semantic correlations.
