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Related lectures (31)

Page 2 of 4

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

Generative Modeling: Then, Now and Tomorrow?

Explores the evolution of generative modeling, from traditional methods to cutting-edge advancements, addressing challenges and envisioning future possibilities.

Deep Generative Models: Variational Autoencoders & GANs

Explores Variational Autoencoders and Generative Adversarial Networks for deep generative modeling.

Machine Learning Fundamentals

Covers fundamental principles, opportunities, and challenges in machine learning.

Hand Pose Estimation

Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.

Coreference Resolution: Models and Evaluation

Explores coreference resolution models, challenges in scoring spans, graph refinement techniques, state-of-the-art results, and the impact of pretrained Transformers.

Transformer: Pre-Training

Explores the Transformer model, from recurrent models to attention-based NLP, highlighting its key components and significant results in machine translation and document generation.

Deep Learning: Convolutional Neural Networks

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

Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Contextual Representations: ELMO and BERT Overview

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.