environment label
e21a7b668ce3ea2c9c964c52d1c9f161-Supplemental-Conference.pdf
Work done during an internship at Tencent AI Lab.
Country:
- Asia > China > Hong Kong (0.04)
- North America > United States > California > San Diego County > San Diego (0.04)
- Asia > Middle East > Iran > Tehran Province > Tehran (0.04)
Technology:
Country:
- Asia > China > Hong Kong (0.04)
- North America > United States > New York > New York County > New York City (0.04)
- North America > United States > California > San Diego County > San Diego (0.04)
- Asia > Middle East > Iran > Tehran Province > Tehran (0.04)
Technology:
Country:
Technology:
Country:
- North America > United States (0.14)
- Asia > China > Beijing > Beijing (0.04)
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Data Science (0.67)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.46)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.45)
Country:
- Asia > China > Beijing > Beijing (0.04)
- North America > United States > Colorado > El Paso County > Colorado Springs (0.04)
- North America > United States > California > Los Angeles County > Long Beach (0.04)
- (4 more...)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.94)
Country:
- North America > United States (0.14)
- Asia > China > Shanghai > Shanghai (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Industry:
Technology:
Country:
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Technology:
LearningInvariantGraphRepresentationsfor Out-of-DistributionGeneralization
Thenweusethevariantsubgraphs,i.e., complements of invariant subgraphs, to infer the latent environment labels.
Country:
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- Asia > China > Jiangsu Province > Changzhou (0.04)
Technology:
Environment Diversification with Multi-head Neural Network for Invariant Learning
Neural networks are often trained with empirical risk minimization; however, it has been shown that a shift between training and testing distributions can cause unpredictable performance degradation. On this issue, a research direction, invariant learning, has been proposed to extract causal features insensitive to the distributional changes. This work proposes EDNIL, an invariant learning framework containing a multi-head neural network to absorb data biases.
Country:
- North America > United States > Minnesota > Hennepin County > Minneapolis (0.14)
- Asia > Taiwan (0.04)
- Oceania > Australia > Victoria > Melbourne (0.04)
- (5 more...)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)