Domain generalization aims to learn a model with good generalization abi...
Partitioning algorithms play a key role in many scientific and engineeri...
Domain generalization (DG) is a branch of transfer learning that aims to...
Unsupervised contrastive learning (UCL) is a self-supervised learning
te...
In this paper, we propose a novel domain generalization (DG) framework b...
Invariance principle-based methods, for example, Invariant Risk Minimiza...
For the Domain Generalization (DG) problem where the hypotheses are comp...
Given a channel having binary input X = (x_1, x_2) having the probabilit...
While capacities of discrete memoryless channels are well studied, it is...
We consider a channel with discrete binary input X that is corrupted by ...
We consider a channel with a binary input X being corrupted by a
continu...
In this paper, we investigate the quantization of the output of a binary...
Given an original discrete source X with the distribution p_X that is
co...
Set partitioning is a key component of many algorithms in machine learni...
The matrix inversion is an interesting topic in algebra mathematics. How...