Search Results for author:
Found 8 papers, 1 papers with code
YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)
Concurrently, we utilized Dice coefficient as the metric for assessing the segmentation outcomes produced by YNetr, having advantage of capturing different frequency information.
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
In this paper, we consider federated reinforcement learning for tabular episodic Markov Decision Processes (MDP) where, under the coordination of a central server, multiple agents collaboratively explore the environment and learn an optimal policy without sharing their raw data.
Real-Time Neural BRDF with Spherically Distributed Primitives
We propose a novel compact and efficient neural BRDF offering highly versatile material representation, yet with very-light memory and neural computation consumption towards achieving real-time rendering.
SieveNet: Selecting Point-Based Features for Mesh Networks
Meshes are widely used in 3D computer vision and graphics, but their irregular topology poses challenges in applying them to existing neural network architectures.
A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval
This paper considers the robust phase retrieval problem, which can be cast as a nonsmooth and nonconvex optimization problem.
Multiplicative Fourier Level of Detail
We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network.
Rubik's Cube Operator: A Plug And Play Permutation Module for Better Arranging High Dimensional Industrial Data in Deep Convolutional Processes
The convolutional neural network (CNN) has been widely applied to process the industrial data based tensor input, which integrates data records of distributed industrial systems from the spatial, temporal, and system dynamics aspects.
Interpretable Neural Networks for Panel Data Analysis in Economics
In this paper, we propose a class of interpretable neural network models that can achieve both high prediction accuracy and interpretability.
