Remove 2020 Remove Algorithm Remove Supervised Learning
article thumbnail

Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

article thumbnail

Test-time Adaptation with Slot-Centric Models

ML @ CMU

Slot-TTA builds on top of slot-centric models by incorporating segmentation supervision during the training phase. ii) We showcase the effectiveness of SSL-based TTA approaches for scene decomposition, while previous self-supervised test-time adaptation methods have primarily demonstrated results in classification tasks.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Offline RL Made Easier: No TD Learning, Advantage Reweighting, or Transformers

BAIR

A demonstration of the RvS policy we learn with just supervised learning and a depth-two MLP. It uses no TD learning, advantage reweighting, or Transformers! Offline reinforcement learning (RL) is conventionally approached using value-based methods based on temporal difference (TD) learning.

article thumbnail

Meet the winners of the Video Similarity Challenge!

DrivenData Labs

Accurate and performant algorithms are critical in flagging and removing inappropriate content. Self-supervision: As in the Image Similarity Challenge , all winning solutions used self-supervised learning and image augmentation (or models trained using these techniques) as the backbone of their solutions.

article thumbnail

Gamification in AI?—?How Learning is Just a Game

Applied Data Science

Then, we will look at three recent research projects that gamified existing algorithms by converting them from single-agent to multi-agent: ?️‍♀️ All the rage was about algorithms for classification. Rahimi and Recht In last year’s ICRL, researchers presented an algorithm that offered a new perspective on PCA: EigenGame.

AI 130
article thumbnail

The Conclusive Machine Learning Engineer Career Path with Free Online Courses

How to Learn Machine Learning

Building a Solid Foundation in Mathematics and Programming To become a successful machine learning engineer, it’s essential to have a strong foundation in mathematics and programming. Mathematics is crucial because machine learning algorithms are built on concepts such as linear algebra, calculus, probability, and statistics.

article thumbnail

Pioneering computer vision: Aleksandr Timashov, ML developer

Dataconomy

I led several projects that dramatically advanced the company’s technological capabilities: Real-time Video Analytics for Security: We developed an advanced system integrating deep learning algorithms with existing CCTV infrastructure. One of the most promising trends in Computer Vision is Self-Supervised Learning.

ML 91