Abstract: We propose a UNet-based foundation model and its self-supervised learning method to address two key challenges: 1)lack of qualified annotated analog layout data, and 2)excessive variety in ...

Recently, federated learning has been successfully applied in fields related to cyber-physical-social systems (CPSSs), owing to its ability to harness decentralized clients for training a global model ...

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its applications. Supervised learning is a type of Machine Learning which learns ...

TraPO is a semi-supervised reinforcement learning framework that bridges unlabeled and labeled samples for training large reasoning models (LRMs). Built upon GRPO, TraPO leverages a small set of ...

Labeling images is a costly and slow process in many computer vision projects. It often introduces bias and reduces the ability to scale large datasets. Therefore, researchers have been looking for ...

Abstract: Deep learning (DL) methods have been widely applied to synthetic aperture radar (SAR) land cover classification. The complexity of SAR data and the limited availability of labeled samples ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

Accurate mapping of the spatial distribution of diverse cell types is essential for understanding the cellular organization of brain. However, the cellular heterogeneity and the substantial cost of ...