Statistical Learning
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment.
The Implicit Bias of Gradient Descent on Separable Multiclass Data
However, there is a noticeable gap in analysis for multiclass classification, with only a handful of results which themselves are restricted to the cross-entropy loss.
Efficient Hyper-parameter Optimization with Cubic Regularization
As hyper-parameters are ubiquitous and can significantly affect the model performance, hyper-parameter optimization is extremely important in machine learning.
Towards Distribution-Agnostic Generalized Category Discovery
While several previous works have focused on classifying close-set samples and detecting open-set samples during testing, it's still essential to be able to classify unknown subjects as human beings.
Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning
Several recent works have attempted to deal with the above issues, which concentrate on the following two aspects.
UPS: Unified Projection Sharing for Lightweight Single-Image Super-resolution and Beyond Kun Zhou
To date, Transformer-based frameworks have demonstrated impressive results in single-image super-resolution (SISR).
Conditional independence testing under misspecified inductive biases
Then, we study the performance of regression-based CI tests under misspecified inductive biases.
