Lectures related to Meta-learning (computer science)

Neural Network Training

Covers the training process of a neural network, including feedforward, cost function, gradient checking, and visualization of hidden layers.

Learning in the Presence of Distribution Shifts: How does the Geometry of Perturbations Play a Role?

Explores learning challenges with distribution shifts and perturbation geometry, focusing on robust classifiers and natural variation modeling.

Modeling Autonomous Systems: Theory and Tools

Explores the significance of modeling and predicting uncertain environments for ensuring safe and high-performance autonomy in modern autonomous systems.

Deep Learning

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

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Transformer Networks: Self-Attention

Explains Transformer networks and self-attention layers for mapping inputs and multi-head attention.

Word Embeddings: Models and Learning

Explores word embeddings, context importance, and learning algorithms for creating new representations.

Deep Learning: Exploring Vision and Language Transformers

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Contrastive losses: Word2Vec and Skip-gram

Covers contrastive losses in Word2Vec and Skip-gram models, negative sampling, Noise Contrastive Estimation, and InfoNCE/CPC.

Transformers in Vision: Applications and Architectures

Covers the impact of transformers in computer vision, discussing their architecture, applications, and advancements in various tasks.

Deep Learning: Graphs and Transformers Overview

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Reward-based Learning: Cellular Mechanisms

Explores reward-based learning in neuronal networks and the role of dopamine in synaptic plasticity.

Reinforcement Learning: Reward-based Learning

Explores artificial neural networks, reward information in the brain, animal conditioning, deep reinforcement learning, and a quiz on rewards.

Pretraining Sequence-to-Sequence Models: BART and T5

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

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Photonic Extreme Learning Machines: Reservoir Computing Techniques

Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.

Expectation Maximization and Clustering

Covers the Expectation Maximization algorithm and clustering techniques, focusing on Gibbs Sampling and detailed balance.

Cognitive Reflection Test and Learning Methods

Explores learning methods, effect sizes, and cognitive assessment in education.

Neural Word Embeddings: Learning Representations for Natural Language

Covers neural word embeddings and methods for learning word representations in natural language processing.