Search Results for author:
Found 16 papers, 5 papers with code
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification
To harness such information to better serve customers, in this paper, we created a machine learning approach to automatically identify product issues and uncover root causes from the customer feedback text.
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers
Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting.
Evaluating the Generation Capabilities of Large Chinese Language Models
This paper unveils CG-Eval, the first-ever comprehensive and automated evaluation framework designed for assessing the generative capabilities of large Chinese language models across a spectrum of academic disciplines.
Face Image Quality Enhancement Study for Face Recognition
To perform this without experimental bias, we have developed a new protocol for recognition with low quality face photos and validate the performance experimentally.
Facial Landmark Detection Evaluation on MOBIO Database
Our work is first to perform facial landmark detection evaluation on the mobile still data, i. e., face images from MOBIO database.
A Study on the Impact of Face Image Quality on Face Recognition in the Wild
In this paper, we partition face images into three different quality sets to evaluate the performance of deep learning methods on cross-quality face images in the wild, and then design a human face verification experiment on these cross-quality data.
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis
How can we teach a computer to recognize 10, 000 different actions?
MorphGANFormer: Transformer-based Face Morphing and De-Morphing
Semantic face image manipulation has received increasing attention in recent years.
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation
In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information.
Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
Our technical contributions include: 1) We propose a fusion-based few-shot learning (FSL) method to learn discriminative features that can generalize to unseen morphing attack types from predefined presentation attacks; 2) The proposed FSL based on the fusion of the PRNU model and Noiseprint network is extended from binary MAD to multiclass morphing attack fingerprinting (MAF).
Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning
We present such a machine learning approach for analyzing Raman spectra of human and avian viruses.
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies.
Certified Monotonic Neural Networks
In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem. This provides a new general approach for learning monotonic neural networks with arbitrary model structures.
Off-Policy Interval Estimation with Lipschitz Value Iteration
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data.
Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep Learning Approach Considering Dynamic Non-Local Spatial Correlation and Non-Stationary Temporal Dependency
This model builds on sequence to sequence (seq2seq) architecture to capture temporal feature and relies on graph convolution for aggregating spatial information.
A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network
In this paper, we propose a hybrid approach that learns the spatio-temporal dependency in traffic flows and predicts short-term traffic speeds on a road network.
