arXiv:2608. 14288v1 Announce Type: new Abstract: We propose multiple new convex losses for SVM and Neural Networks, applied to binary classification tasks.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2607. 13631v1 Announce Type: new Abstract: The Hessian matrix is an important quantity of interest when it comes to studying the loss landscape and optimization dynamics in deep learning, as well as designing measures of generalization, second-order learning algorithms, etc.
arXiv:2606. 05814v1 Announce Type: new Abstract: The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult.
The Hessian matrix is an important quantity of interest when it comes to studying the loss landscape and optimization dynamics in deep learning, as well as designing measures of generalization, second-order learning algorithms, etc. Prior works have focused on empirical results or pursued a theoretical treatment under overly simplified settings.
arXiv:2607. 16768v1 Announce Type: new Abstract: Learning with noisy labels is a fundamental problem in training reliable deep neural networks.
arXiv:2605. 20347v2 Announce Type: replace Abstract: Labeling a training set is often expensive and susceptible to errors, making the design of robust loss functions for label noise an important problem.
arXiv:2402. 13425v3 Announce Type: replace-cross Abstract: It is becoming increasingly common in regression to train neural networks that model the entire distribution even if only the mean is required for prediction.