Search Results for author:
Found 23 papers, 5 papers with code
CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems
These results highlight the effectiveness of the CREAD framework in watch time prediction in video recommender systems.
Meta-DSP: A Meta-Learning Approach for Data-Driven Nonlinear Compensation in High-Speed Optical Fiber Systems
Non-linear effects in long-haul, high-speed optical fiber systems significantly hinder channel capacity.
Data-Driven Nonlinear TDOA for Accurate Source Localization in Complex Signal Dynamics
The complex and dynamic propagation of oscillations and waves is often triggered by sources at unknown locations.
Accurate RSS-Based Localization Using an Opposition-Based Learning Simulated Annealing Algorithm
The final solution is selected as the more effective of the two outcomes from the SAA, thereby reducing the likelihood of the algorithm becoming trapped in local optima.
Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images
Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible.
DOSnet as a Non-Black-Box PDE Solver: When Deep Learning Meets Operator Splitting
Deep neural networks (DNNs) recently emerged as a promising tool for analyzing and solving complex differential equations arising in science and engineering applications.
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation
Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets.
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction
However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.
GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression
Besides the latent variable unique to each patch, we introduce shared latent variables between patches to construct the global context.
Outpainting by Queries
Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision.
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency.
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.
PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification
Automatic nuclei segmentation and classification play a vital role in digital pathology.
Ranked #4 on
Multi-tissue Nucleus Segmentation
on CoNSeP
AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training
We also propose a novel training algorithm able to align the disentangled content in the latent space to reduce micro-level lossy transformation.
Ordinal UNLOC: Target Localization with Noisy and Incomplete Distance Measures
A main challenge in target localization arises from the lack of reliable distance measures.
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation
The voxel-based view is regular, but sparse, and computation grows cubically when voxel resolution increases.
Ranked #15 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
SGMNet: Learning Rotation-Invariant Point Cloud Representations via Sorted Gram Matrix
Recently, various works that attempted to introduce rotation invariance to point cloud analysis have devised point-pair features, such as angles and distances.
A variable metric mini-batch proximal stochastic recursive gradient algorithm with diagonal Barzilai-Borwein stepsize
In this paper, we propose a variable metric mini-batch proximal stochastic recursive gradient algorithm VM-mSRGBB, which updates the metric using a new diagonal BB stepsize.
RODE-Net: Learning Ordinary Differential Equations with Randomness from Data
To train the RODE-Net, we first estimate the parameters of the unknown RODE using the symbolic networks \cite{long2019pde} by solving a set of deterministic inverse problems based on the measured data, and use a generative adversarial network (GAN) to estimate the true distribution of the RODE's parameters.
Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)
Automated learning of a Boolean network and Boolean functions, from data, is a challenging task due in part to the large number of unknowns (including both the structure of the network and the functions) to be estimated, for which a brute force approach would be exponentially complex.
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images.
Y-net: 3D intracranial artery segmentation using a convolutional autoencoder
Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system.
Bayesian Optical Flow with Uncertainty Quantification
Optical flow refers to the visual motion observed between two consecutive images.
