Stefan Roth: Robust Scene Analysis
Explore robust scene analysis techniques, focusing on optical flow estimation, motion segmentation, and uncertainty in computer vision tasks. Learn about challenges, objectives, and innovative approaches.
Artificial Intelligence and the Future of Humanity
Explore AI's impact on society, work, and technology. Learn about neural networks, job automation, ethical concerns, and future possibilities from AI expert Andrew Ng.
Machine Learning in Cybersecurity - Boon or Boondoggle
Explore machine learning's impact on cybersecurity with Dr. Zulfikar Ramzan. Learn about supervised and unsupervised learning, challenges, and applications in DevOps while gaining insights from a leading industry expert.
Introduction to Machine Learning - Lecture 2
Explore key machine learning concepts and their applications in healthcare, bridging the gap between computational modeling and clinical problem-solving.
Unsupervised ML - Clustering, Dimensionality Reduction, Applications by Rahul Siddharthan
Explore unsupervised machine learning techniques for clustering and dimensionality reduction, with practical applications in data analysis and pattern discovery.
Adversarial Strategies for Anomaly Detection and Image Restoration - SIAM-IS Virtual Seminar
Adversarial approaches in imaging: anomaly detection using GANs and image restoration techniques for inpainting and colorization. Explores efficient methods for unsupervised learning and data distribution analysis.
Generalized Tensor Decomposition - Utility for Data Analysis
Explore tensor decomposition methods beyond sum of squares error, including KL divergence and logistic odds, for improved data analysis in various fields like criminology and neuroscience.
Measuring Interpretability in AI - Transforming Black Box Systems into Transparent Tools
Explore novel metrics for measuring AI interpretability in unsupervised and supervised methods, enhancing transparency in topic models and question-answering systems for broader applications.
V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video
Explore V-JEPA, an unsupervised learning method for video data using latent representation prediction. Learn about its architecture, concept, and performance in various visual tasks.
Unsupervised Learning of Group Invariant and Equivariant Representations
Explore unsupervised learning of group invariant and equivariant representations. Gain insights into symmetry in data, practical examples, and results for graphs and various symmetries.
Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes
Explore unsupervised machine learning for fragment-based hit discovery in drug development. Learn about FRESCO method, pharmacophore modeling, and its application in COVID-19 research.
Identifiable Deep Generative Models via Sparse Decoding
Explore sparse VAE for unsupervised learning, uncovering identifiable latent factors in high-dimensional data. Discover theoretical guarantees and practical applications in various domains.
Guaranteed Training of Neural Networks Using Tensor Methods - 2015
Explore guaranteed neural network training using tensor methods. Learn about overcoming non-convexity, risk bounds, and combining unsupervised learning with supervised tasks.
Deep Learning of Generative Models - 2014
Explore generative models in deep learning with Yoshua Bengio, covering motivations, applications, and fundamental challenges in AI and machine learning.
Learning Energy-Based Models of High-Dimensional Data - 2002
Explore energy-based models for high-dimensional data analysis. Learn about deterministic networks, perceptual inference, and efficient learning techniques for large-scale models.
Weak and Strong Learning of Context-Free Grammars - 2012
Explore recent advancements in unsupervised learning of context-free grammars and the transition from weak to strong learning algorithms for accurate syntactic structure prediction.
Unsupervised Learning and Advanced Topics - Lecture 4
Explore unsupervised learning techniques, model deployment, and ethical considerations in petroleum engineering and energy transition contexts.
NYU Deep Learning
Comprehensive deep learning course covering neural networks, convolutional and recurrent architectures, energy-based models, self-supervised learning, and advanced topics like transformers and graph networks.
Data Science Way to Deal With Advanced Threats
Explore data science methods for advanced threat detection in SOCs, combining unsupervised and supervised learning to reduce false positives and streamline anomaly triage using ML and interactive visualizations.
Training Machines for Application Scanning in Retro Style
Explore machine learning for offensive security testing, focusing on building an intelligent application scanner using supervised and unsupervised learning techniques to improve web application vulnerability detection.
Data Science Tools and Techniques for the Blue Team
Explore data science tools and techniques for cybersecurity, including statistical analysis and machine learning, to enhance threat detection and defense capabilities in large-scale corporate environments.