Search Results for author:
Found 18 papers, 6 papers with code
Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models
Diffusion models (DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high-quality and realistic data samples.
Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis
Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video.
The Causal Impact of Credit Lines on Spending Distributions
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales.
UTBoost: A Tree-boosting based System for Uplift Modeling
Uplift modeling refers to the set of machine learning techniques that a manager may use to estimate customer uplift, that is, the net effect of an action on some customer outcome.
DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series
The tensorized LSTM assigns each variable with a unique hidden state making up a matrix $\mathbf{h}_t$, and the standard LSTM models all the variables with a shared hidden state $\mathbf{H}_t$.
TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety
Large Language Models (LLMs) have shown remarkable effectiveness in various general-domain natural language processing (NLP) tasks.
Deep into The Domain Shift: Transfer Learning through Dependence Regularization
This paper proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals.
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?
ChatGPT embarks on a new era of artificial intelligence and will revolutionize the way we approach intelligent traffic safety systems.
On Calibrating Semantic Segmentation Models: Analyses and An Algorithm
Then, we study popular existing calibration methods and compare them with selective scaling on semantic segmentation calibration.
Robust Causal Learning for the Estimation of Average Treatment Effects
Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue.
Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information
In this paper, we propose a moderately-balanced representation learning (MBRL) framework based on recent covariates balanced representation learning methods and orthogonal machine learning theory.
Deep Learning-based Segmentation of Cerebral Aneurysms in 3D TOF-MRA using Coarse-to-Fine Framework
The segmentation metrics we used include DSC, HD, and VS.
An Automatic Detection Method Of Cerebral Aneurysms In Time-Of-Flight Magnetic Resonance Angiography Images Based On Attention 3D U-Net
Conclusions:Compared with the results of our previous studies and other studies, the method in this paper achieves a very competitive sensitivity with less training data and maintains a low false positive rate. As the only method currently using 3D U-Net for aneurysm detection, it proves the feasibility and superior performance of this network in aneurysm detection, and also explores the potential of the channel attention mechanism in this task.
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19
Next, we use simulated observation sequences to query the simulation system to retrieve simulated projection sequences as knowledge.
The Causal Learning of Retail Delinquency
Classical estimators overlook the confounding effects and hence the estimation error can be magnificent.
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model
The other is that the number of images used for the knowledge distillation should be small; otherwise, it violates our expectation of reducing the dependence on large-scale datasets.
Deep learning based automatic segmentation of lumbosacral nerves on non-contrast CT for radiographic evaluation: a pilot study
Pixel accuracy, IoU, and Dice score are used to assess the segmentation performance of lumbosacral structures.
