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How Cheap Mortgages Transformed Poland’s Real Estate Market | by Lukasz Szubelak | Jan, 2025

Insights from a synthetic control group 16 min read · 10 hours ago Photo by Maria Ziegler on UnsplashReal estate is a bedrock of modern economies, serving as both a tangible asset and an essential component of wealth accumulation for individuals and investment portfolios. Real estate price fluctuations have far-reaching implications, influencing…

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Advancing AI Reasoning: Meta-CoT and System 2 Thinking | by Kaushik Rajan | Jan, 2025

How Meta-CoT enhances system 2 reasoning for complex AI challenges Image created by the author using Generative AI (Flux-pro)What makes a language model smart? Is it predicting the next word in a sentence ‒ or handling tough reasoning tasks that challenge even bright humans? Today’s Large Language Models (LLMs) create smooth text plus solve simple…

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A Beginner’s 12-Step Visual Guide to Understanding NeRF: Neural Radiance Fields for Scene Representation and View Synthesis | by Aqeel Anwar | Jan, 2025

A basic understanding of NeRF’s workings through visual representations Who should read this article? This article aims to provide a basic beginner level understanding of NeRF’s workings through visual representations. While various blogs offer detailed explanations of NeRF, these are often geared toward readers with a strong technical background in volume rendering and 3D graphics.…

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Content-Adaptive Tokenizer (CAT): An Image Tokenizer that Adapts Token Count based on Image Complexity, Offering Flexible 8x, 16x, or 32x Compression

One of the major hurdles in AI-driven image modeling is the inability to account for the diversity in image content complexity effectively. The tokenization methods so far used are static compression ratios where all images are treated equally, and the complexities of images are not considered. Due to this reason, complex images get over-compressed and…

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From Latent Spaces to State-of-the-Art: The Journey of LightningDiT

Latent diffusion models are advanced techniques for generating high-resolution images by compressing visual data into a latent space using visual tokenizers. These tokenizers reduce computational demands while retaining essential details. However, such models suffer from a critical challenge: increasing the dimensions of the token feature increases reconstruction quality but decreases image generation quality. It thus…

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Predicting a Ball Trajectory. Polynomial Fit in Python with NumPy | by Florian Trautweiler | Jan, 2025

Polynomial Fit in Python with NumPy Ball Tracking and Trajectory PredictionIn a previous project I visualized the trajectory of a ball that I threw vertically into the air with a real-time position, velocity and acceleration plot. Extending upon this project, I wanted to calculate and visualize a trajectory prediction based on a simple physics model.…

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ByteDance Research Introduces 1.58-bit FLUX: A New AI Approach that Gets 99.5% of the Transformer Parameters Quantized to 1.58 bits

Vision Transformers (ViTs) have become a cornerstone in computer vision, offering strong performance and adaptability. However, their large size and computational demands create challenges, particularly for deployment on devices with limited resources. Models like FLUX Vision Transformers, with billions of parameters, require substantial storage and memory, making them impractical for many use cases. These limitations…

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Genie 2: A large-scale foundation world model

Acknowledgements Genie 2 was led by Jack Parker-Holder with technical leadership by Stephen Spencer, with key contributions from Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi and Jessica Yung, and contributions from Michael Dennis, Sultan Kenjeyev and Shangbang Long. Yusuf Aytar, Jeff Clune, Sander Dieleman,…

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