Do ImageNet Classifiers Generalize?

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Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

Explores the generalization of ImageNet classifiers and the challenges in achieving reliable machine learning models.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Explores robustness in visual intelligence, addressing distribution shifts, failure examples, and strategies for improvement.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Covers the design and optimization of neural network models in deep learning.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.