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AI Breathes New Life into Early Mickey Mouse: Exploring the Public Domain

AI experimenters have quickly taken advantage of three early Mickey Mouse cartoons entering the public domain in the US, using an AI model trained on those cartoons to create new still images of Mickey Mouse, Minnie Mouse, and Peg Leg Pete. While the results are sometimes garbled, this early experimentation showcases the potential of integrating public domain characters into the AI space.

Unveiling a Hidden Bias: Enhancing Decision Trees and Random Forests

Recent research explores how decision trees and random forests, commonly used in machine learning, suffer from bias due to the assumption of continuity in features. The study proposes simple techniques to mitigate this bias, with findings showing a 0.2 percentage point deterioration in performance when attributes are mirrored.

Revolutionizing Music AI: 3 Breakthroughs to Expect in 2024

2024 could be the tipping point for Music AI, with breakthroughs in text-to-music generation, music search, and chatbots. However, the field still lags behind Speech AI, and advancements in flexible and natural source separation are needed to revolutionize music interaction through AI.

Accelerating Deep Learning: Unleashing the Power of Momentum, AdaGrad, RMSProp & Adam

This article explores acceleration techniques in neural networks, emphasizing the need for faster training due to the complexity of deep learning models. It introduces the concept of gradient descent and highlights the limitations of its slow convergence rate. The article then introduces Momentum as an optimization algorithm that uses an exponentially moving average to achieve faster convergence.

Efficient Matrix Inversion Using QR Decomposition in C#

The article discusses the author's implementation of matrix inverse using QR decomposition and highlights the different algorithms and variations involved in computing the inverse of a matrix. The demo showcases the computation of a 4x4 matrix's inverse and verifies the result by multiplying it with the original matrix to obtain the identity matrix.

The Power of Gaussian Splatting: Revolutionizing 3D Representations

Gaussian splatting is a fast and interpretable method for representing 3D scenes without neural networks, gaining popularity in a world obsessed with AI models. It uses 3D points with unique parameters to closely match renders to known dataset images, offering a refreshing alternative to complex and opaque methods like NeRF.