Search Results for author:
Found 15 papers, 2 papers with code
Outlier Detection Using Generative Models with Theoretical Performance Guarantees
We propose an outlier detection approach for reconstructing the ground-truth signals modeled by generative models under sparse outliers.
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao
Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue.
An End-to-End Cascaded Image Deraining and Object Detection Neural Network
Firstly, the gap between the low-level vision task represented by rain removal and the high-level vision task represented by object detection is significant, and the low-level vision task can hardly contribute to the high-level vision task.
Image Deraining and Denoising Convolutional Neural Network ForAutonomous Driving
Perception plays an important role in reliable decision-making for autonomous vehicles.
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection
In this paper, we propose a transfer learning framework that adapts a control policy for excessive disturbance rejection of an underwater robot under dynamics model mismatch.
Heterogeneous Graph Transformer for Graph-to-Sequence Learning
The graph-to-sequence (Graph2Seq) learning aims to transduce graph-structured representations to word sequences for text generation.
Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization
In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents.
AMR-To-Text Generation with Graph Transformer
Abstract meaning representation (AMR)-to-text generation is the challenging task of generating natural language texts from AMR graphs, where nodes represent concepts and edges denote relations.
Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal Recovery
We study the robust recovery problem for the spectrally sparse signal under the fully observed setting, which is about recovering $\boldsymbol{x}$ and a sparse corruption vector $\boldsymbol{s}$ from their sum $\boldsymbol{z}=\boldsymbol{x}+\boldsymbol{s}$.
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances
This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OBserver Network (DOB-Net), for robots operating in environments where disturbances are unknown and time-varying, and may frequently exceed robot control capabilities.
T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion
Our model uses shared attention layers for encoder and decoder, which make the most of the contextual clues, and a latent variable for learning the distribution of coherent story plots.
Blind Hyperspectral-Multispectral Image Fusion via Graph Laplacian Regularization
Fusing a low-resolution hyperspectral image (HSI) and a high-resolution multispectral image (MSI) of the same scene leads to a super-resolution image (SRI), which is information rich spatially and spectrally.
Outlier Detection using Generative Models with Theoretical Performance Guarantees
In this paper, we propose a generative model neural network approach for reconstructing the ground truth signals under sparse outliers.
PKUSUMSUM : A Java Platform for Multilingual Document Summarization
PKUSUMSUM is a Java platform for multilingual document summarization, and it sup-ports multiple languages, integrates 10 automatic summarization methods, and tackles three typical summarization tasks.
