Search Results for author:
Found 6 papers, 4 papers with code
Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
In this work, we provide the convergence analysis of natural gradient descent (NGD) in training two-layer PINNs, demonstrating that the learning rate can be $\mathcal{O}(1)$, and at this rate, the convergence rate is independent of the Gram matrix.
Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks
In this paper, we provide convergence analysis for the IGD in training over-parameterized two-layer PINNs.
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples.
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances.
Ranked #4 on
Few-Shot Object Detection
on MS-COCO (1-shot)
CDNet: Centripetal Direction Network for Nuclear Instance Segmentation
Specifically, we define the centripetal direction feature as a class of adjacent directions pointing to the nuclear center to represent the spatial relationship between pixels within the nucleus.
How many infections of COVID-19 there will be in the "Diamond Princess"-Predicted by a virus transmission model based on the simulation of crowd flow
Objectives: Simulate the transmission process of COVID-19 in a cruise ship, and then to judge how many infections there will be in the 3711 people in the "Diamond Princess" and analyze measures that could have prevented mass transmission.
Physics and Society Computers and Society
