February 2023

Easily Boost Your Neural Network’s Robustness To Attacks With Label Smoothing

“Adversarial Robustness via Label Smoothing” is a research paper by Jiang et al. that explores the use of label smoothing as a method to improve the robustness of neural networks against adversarial attacks. Adversarial attacks are a type of attack on a machine learning model in which an attacker adds small perturbations to the input […]

Easily Boost Your Neural Network’s Robustness To Attacks With Label Smoothing Read More »

Revolutionizing Linear Inverse Problems: A New Deep Learning Approach

The authors propose a deep learning approach for solving universal linear inverse problems. Linear inverse problems arise in many fields of science and engineering, and refer to the problem of recovering a signal from a set of measurements that are related to the signal through a linear transformation. The authors demonstrate the effectiveness of their

Revolutionizing Linear Inverse Problems: A New Deep Learning Approach Read More »

New Meta-Learning Approach Improves Few-Shot Learning

The authors propose a novel approach to few-shot learning using meta-learning. Few-shot learning is a challenging problem in machine learning where a model must learn to classify new objects with only a small number of examples. The authors demonstrate the effectiveness of their approach by comparing it to several existing state-of-the-art methods for few-shot learning

New Meta-Learning Approach Improves Few-Shot Learning Read More »