Labeling Images for Semantic Segmentation Using Label Studio
Learn to label images for semantic segmentation using Label Studio, covering installation, project setup, annotation, and exporting in this practical tutorial.
Semi-Supervised Learning with GANs in Keras
Explore semi-supervised learning with GANs in Keras, focusing on training discriminators as classifiers using limited labeled data for improved accuracy compared to traditional CNNs.
Semi-Supervised Learning with GANs
Explore semi-supervised learning with GANs, focusing on training discriminators as classifiers using limited labeled data for improved accuracy compared to traditional CNNs.
Single Image Super Resolution Using SRGAN
Explore single image super-resolution using SRGAN, covering key concepts, perceptual loss, content loss, and network architectures for enhanced image quality.
Unpaired Image to Image Translation Using CycleGAN - An Introduction
Introduction to CycleGAN for unpaired image-to-image translation, covering key concepts, architecture, and training process. Explores discriminator and generator components, focusing on PatchGAN and instance normalization.
Image to Image Translation Using Pix2Pix GAN
Learn image-to-image translation using Pix2Pix GAN, exploring key concepts, generator and discriminator architectures, and practical applications in satellite imagery and scientific image generation.
Compositing Tips in Photoshop
Learn advanced Photoshop compositing techniques from expert Jesus Ramirez. Topics include background removal, rim lighting, image harmonization, color adjustments, hair refinement, and blending for realistic results.
Steps Towards More Human-Like Learning in Machines - Josh Tenenbaum
Explore how machines can achieve more human-like learning through probabilistic programming, intuitive psychology, and simulation engines. Discover insights on core knowledge and one-shot learning in AI.
Modularity, Attention and Credit Assignment Computations - Anirudh Goyal
Explore modularity, attention, and credit assignment in computational models, focusing on complex generalization, independent mechanisms, and selective activation for improved learning and problem-solving.
Meta-Learning - Why It’s Hard and What We Can Do - Ke Li
Explore meta-learning challenges and solutions with Ke Li, covering optimization, model selection, and program induction in this theoretical machine learning seminar.
Your Brain on Energy-Based Models - Applying and Scaling EBMs to Problems
Explore energy-based models in machine learning, covering applications, scaling techniques, and their impact on classification, calibration, and robustness in AI systems.
Re-Imagining the Social Sciences in the Age of AI - March 4, 2020
Explore the intersection of AI and social sciences through expert talks on mate selection decision-making and human behavior as AI's next frontier, followed by a moderated discussion and audience Q&A.
Understanding Deep Neural Networks - From Generalization to Interpretability
Explore deep neural networks, focusing on generalization and interpretability. Delve into graph convolutional networks, spectral filtering, and transferability. Examine fundamental questions and relevance mapping in machine learning.
Nonlinear Independent Component Analysis - Aapo Hyvärinen
Explore nonlinear independent component analysis, its applications in AI and unsupervised learning, and recent advancements in deep latent variable models with Aapo Hyvärinen.
Efficient Robot Skill Learning via Grounded Simulation Learning, Imitation Learning - Peter Stone
Explore efficient robot skill learning through grounded simulation, imitation learning, and off-policy reinforcement learning. Discover innovative approaches for transferring skills from simulation to real-world robots.
The Challenges of Model-Based Reinforcement Learning and How to Overcome Them - Csaba Szepesvári
Explore challenges in model-based reinforcement learning, including modeling errors and efficient planning, and discover strategies to overcome them in complex environments.
Neural SDEs - Deep Generative Models in the Diffusion Limit - Maxim Raginsky
Explore deep generative models through stochastic control, diffusion limits, and neural nets. Learn about efficient sampling, expressiveness quantification, and unbiased simulation techniques.
Generative Modeling by Estimating Gradients of the Data Distribution - Stefano Ermon
Explore generative modeling through gradient estimation of data distributions, covering deep energy-based models, score matching, and innovative sampling techniques for improved AI generation.
Algorithmic Cruelty and the Hidden Costs of Ghost Work - Mary Gray
Explore the hidden world of digital labor, examining automation's paradoxes, algorithmic impacts, and the social costs of today's platform-based work economy.
How Will Artificial Intelligence Change Ethics? - Pedro Domingos
Explore AI's impact on privacy, fairness, work, ethics, warfare, governance, and social media, examining how it challenges our understanding of human uniqueness.
Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin
Explore efficient reinforcement learning with linear function approximation, covering key concepts like sample efficiency, value-based algorithms, and exploration in multi-armed bandits and linear MDPs.