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The Series Finale of Online Education: Coursera Acquires Udemy

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Representation Learning

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International Mathematical Union
Theory of Graph Neural Networks: Representation and Learning

Explore the theory behind Graph Neural Networks, covering approximation, learning properties, and connections to graph isomorphism, equivariant functions, and dynamic programming.
- YouTube
- 46 minutes
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- Free Video
Simons Institute
Representation Learning of Grounded Language and Knowledge - With and Without End-to-End Learning

Explore representation learning for grounded language and knowledge, covering end-to-end learning, procedural language challenges, and applications in cooking instructions and dialogue generation.
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Broad Institute
Physics Informed Representation Learning for Therapeutics

Dive into physics-informed representation learning for therapeutics with MIT Assistant Professor Rafael Gomez-Bombarelli, exploring innovative approaches in drug discovery and development.
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Kavli Institute for Theoretical Physics
Representation Learning and Custom Loss Functions for Atmospheric Data - Christian Lessig

Explores representation learning and custom loss functions for atmospheric data analysis, advancing climate science through machine learning techniques to extract insights from complex Earth system observations.
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Institute for Pure & Applied Mathematics (IPAM)
Multiview and Self-Supervised Representation Learning - Nonlinear Mixture Identification

Explore nonlinear mixture identification in multiview and self-supervised representation learning. Gain insights into extracting essential information from high-dimensional data for improved AI applications.
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Inside Livermore Lab
Tensor Factorization for Biomedical Representation Learning

Explore tensor factorization for biomedical representation learning, addressing challenges in healthcare data and improving predictive power for various downstream tasks.
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DataLearning@ICL
Is There a Place for Representation Learning in Generative AI?

Explore the intersection of Deep Learning, Representation Learning, and Generative AI, examining their relationships and potential synergies in advancing AI technology.
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VinAI
Representation Learning with Graph Autoencoders and Applications to Music Recommendation

Explore graph autoencoders for node embedding, addressing scalability and real-world graph challenges. Learn about advancements in GAE models and their application to music recommendation at Deezer.
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ICTP Condensed Matter and Statistical Physics
Representation Learning for Quantum Systems

Explore advanced techniques for representation learning in quantum systems, focusing on cutting-edge approaches and their applications in condensed matter physics.
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AI Doctoral Academy
Variational Audio-Visual Representation Learning - Lecture 34

Explore advanced concepts in variational learning techniques for audio-visual data representation, focusing on cutting-edge AI methodologies and their practical applications.
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Simons Institute
Generalization and Equilibrium in Generative Adversarial Nets - GANs

Explore the theoretical foundations of GANs, focusing on generalization and equilibrium concepts in representation learning with Princeton's Sanjeev Arora.
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Stanford Seminar - Representation Learning for Autonomous Robots, Anima Anandkumar

I will describe some key ingredients to enable this: (1) robust self-supervised learning (2) uncertainty awareness (3) compositionality. We utilize NVIDIA Isaac for GPU-accelerated robot learning at scale on a variety of tasks and domains.
- YouTube
- 56 minutes
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- Free Online Course
Simons Institute
Evaluating Neural Network Representations Against Human Cognition

Explore neural network representations and human cognition, comparing categorization models, psychological spaces, and relational similarities to enhance understanding of cognitive processes.
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Simons Institute
The Missing Signal

Explore representation learning and its applications in AI research with insights from Facebook's leading expert.
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Association for Computing Machinery (ACM)
Graph Mining Hamilton

Learn graph representation techniques for node classification, link prediction, and graph classification. Explore deep learning on graphs, GNNs, and their applications in drug repurposing and recommender systems.
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