Search Results for author:
Found 15 papers, 7 papers with code
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification
Few-shot image classification aims to classify images from unseen novel classes with few samples.
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL.
Ranked #1 on
Cross-Domain Few-Shot
on Plantae
ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning
Concretely, to solve the data imbalance problem between the source data with sufficient examples and the auxiliary target data with limited examples, we build our model under the umbrella of multi-expert learning.
A Block-Based Adaptive Decoupling Framework for Graph Neural Networks
Graph neural networks (GNNs) with feature propagation have demonstrated their power in handling unstructured data.
Physics-Informed Statistical Modeling for Wildfire Aerosols Process Using Multi-Source Geostationary Satellite Remote-Sensing Data Streams
Increasingly frequent wildfires significantly affect solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation to the earth.
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning
The key challenge of CD-FSL lies in the huge data shift between source and target domains, which is typically in the form of totally different visual styles.
Ranked #2 on
Cross-Domain Few-Shot
on CUB
Learning To Memorize Feature Hallucination for One-Shot Image Generation
In this paper, we propose a novel model to explicitly learn and memorize reusable features that can help hallucinate novel category images.
How COVID-19 has Impacted American Attitudes Toward China: A Study on Twitter
Past research has studied social determinants of attitudes toward foreign countries.
Multi-Party Dual Learning
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data.
Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework
Since the existing naive model parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogy multi-party learning as a many-to-one knowledge sharing problem.
Large-scale Quantitative Evidence of Media Impact on Public Opinion toward China
Do mass media influence people's opinion of other countries?
Multitask machine learning of collective variables for enhanced sampling of rare events
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics.
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features.
A Survey on Dynamic Network Embedding
Real-world networks are composed of diverse interacting and evolving entities, while most of existing researches simply characterize them as particular static networks, without consideration of the evolution trend in dynamic networks.
Social and Information Networks Physics and Society
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions.
