Search Results for author:
Found 22 papers, 2 papers with code
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
End-to-end visual-based imitation learning has been widely applied in autonomous driving.
K-UNN: k-Space Interpolation With Untrained Neural Network
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data.
Gaze Training by Modulated Dropout Improves Imitation Learning
The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior.
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments
Vision-based autonomous driving through imitation learning mimics the behaviors of human drivers by training on pairs of data of raw driver-view images and actions.
An Efficient Target Detection and Recognition Method in Aerial Remote-sensing Images Based on Multiangle Regions-of-Interest
Recently, deep learning technology have been extensively used in the field of image recognition.
Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze
Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies.
CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation
Forecasting human trajectories is critical for tasks such as robot crowd navigation and autonomous driving.
HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction
Fully investigating the social interactions within the crowd is crucial for accurate pedestrian trajectory prediction.
AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention
We suggest that introducing an attention mechanism to infer the importance of different neighbors is critical for accurate trajectory prediction in scenes with varying crowd size.
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
To provide a solution to this problem, we propose a novel branched network G-CIL for the navigation policy learning.
Dynamic Parameterized Network for CTR Prediction
Learning to capture feature relations effectively and efficiently is essential in click-through rate (CTR) prediction of modern recommendation systems.
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction
Click-through rate (CTR) Prediction is of great importance in real-world online ads systems.
On the Adaptation to Concept Drift for CTR Prediction
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying.
PS-Net: Learned Partially Separable Model for Dynamic MR Imaging
In this paper, we propose a learned low-rank method for dynamic MR imaging.
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving
In this work, we propose a novel framework PCDF(Parallel-Computing Distributed Framework), allowing to split the computation cost into three parts and to deploy them in the pre-module in parallel with the retrieval stage, the middle-module for ranking ads, and the post-module for re-ranking ads with external items.
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Streaming data has the characteristic that the underlying distribution drifts over time and may recur.
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI
We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable.
Confidence Ranking for CTR Prediction
Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e. g. ads and recommendation systems.
Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation
In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both $k$-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations.
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis
In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance.
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging
Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging.
Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction
The complementary information can contribute to image reconstruction.
