Search Results for author:
Found 13 papers, 7 papers with code
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows.
Spatio-Temporal-Decoupled Masked Pre-training: Benchmarked on Traffic Forecasting
Accurate forecasting of multivariate traffic flow time series remains challenging due to substantial spatio-temporal heterogeneity and complex long-range correlative patterns.
Ranked #1 on
Traffic Prediction
on PEMS-BAY
(using extra training data)
GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation
As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results.
A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization
Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated to be effective in solving multi-criteria decision-making problems.
Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks
In this paper, we primarily focus on understanding the data preprocessing pipeline for DNN Training in the public cloud.
XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis
Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries.
BiFeat: Supercharge GNN Training via Graph Feature Quantization
We identify the main accuracy impact factors in graph feature quantization and theoretically prove that BiFeat training converges to a network where the loss is within $\epsilon$ of the optimal loss of uncompressed network.
CNN-Augmented Visual-Inertial SLAM with Planar Constraints
The CNN depth effectively bootstraps the back-end optimization of SLAM and meanwhile the CNN uncertainty adaptively weighs the contribution of each feature point to the back-end optimization.
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner.
GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping
We present a robust and accurate depth refinement system, named GeoRefine, for geometrically-consistent dense mapping from monocular sequences.
Self-Supervised Vessel Segmentation via Adversarial Learning
Vessel segmentation is critically essential for diagnosinga series of diseases, e. g., coronary artery disease and retinal disease.
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time.
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks.
