Robustness and Diffusion Models

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The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Score-Based Generative Models

Delves into score-based generative models, exploring learning natural distributions and the impact of neural network architecture on robustness.

Deep Learning: Data Representations and Neural Networks

Explores data representations, histograms, neural networks, and deep learning concepts.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Deep Learning Building Blocks: Linear Layers

Explains the fundamental building blocks of deep learning, focusing on linear layers and activation functions.

Deep Splines: Unifying Framework for Deep Neural Networks

Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.

Recurrent Neural Networks: Training and Challenges

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.