The Hidden Convex Optimization Landscape of Deep Neural Networks

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Introduces machine learning concepts, from basics to advanced neural networks.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Covers brainstorming options for smart operation changes, heat recovery, and PV panel performance.

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

The session explains how momentum accelerates gradient descent, presents the theory of nonlinear conjugate gradients on manifolds, compares several beta‑rule updates, discusses line‑search and preconditioner choices, and shows how to employ the Manopt library for Riemannian optimization without explicit Hessians.

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Explains how to solve linear equations using the least squares method to minimize errors in the system.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Explores over-learning, generalization, and under-learning in machine learning models.

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Covers the calculation of Taylor polynomials and their applications in limits and derivatives of functions from R² to R.

Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Delves into structures in non-convex optimization, emphasizing scalable optimization for deep learning.

Covers the fundamentals of multilayer neural networks and deep learning.

Explores non-convex optimization in deep learning, covering critical points, SGD convergence, saddle points, and adaptive gradient methods.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.