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research#backpropagation📝 BlogAnalyzed: Jan 18, 2026 08:45

XOR Solved! Deep Learning Journey Illuminates Backpropagation

Published:Jan 18, 2026 08:35
1 min read
Qiita DL

Analysis

This article chronicles an exciting journey into the heart of deep learning! By implementing backpropagation to solve the XOR problem, the author provides a practical and insightful exploration of this fundamental technique. Using tools like VScode and anaconda creates an accessible entry point for aspiring deep learning engineers.
Reference

The article is based on conversations with Gemini, offering a unique collaborative approach to learning.

Fast Algorithm for Stabilizer Rényi Entropy

Published:Dec 31, 2025 07:35
1 min read
ArXiv

Analysis

This paper presents a novel algorithm for calculating the second-order stabilizer Rényi entropy, a measure of quantum magic, which is crucial for understanding quantum advantage. The algorithm leverages XOR-FWHT to significantly reduce the computational cost from O(8^N) to O(N4^N), enabling exact calculations for larger quantum systems. This is a significant advancement as it provides a practical tool for studying quantum magic in many-body systems.
Reference

The algorithm's runtime scaling is O(N4^N), a significant improvement over the brute-force approach.

Analysis

This post details an update on NOMA, a system language and compiler focused on implementing reverse-mode autodiff as a compiler pass. The key addition is a reproducible benchmark for a "self-growing XOR" problem. This benchmark allows for controlled comparisons between different implementations, focusing on the impact of preserving or resetting optimizer state during parameter growth. The use of shared initial weights and a fixed growth trigger enhances reproducibility. While XOR is a simple problem, the focus is on validating the methodology for growth events and assessing the effect of optimizer state preservation, rather than achieving real-world speed.
Reference

The goal here is methodology validation: making the growth event comparable, checking correctness parity, and measuring whether preserving optimizer state across resizing has a visible effect.

MM15 - Save Your Servants!: Barker, Blatty & Writers In Hell

Published:Oct 23, 2024 18:03
1 min read
NVIDIA AI Podcast

Analysis

This NVIDIA AI Podcast episode, part of the Movie Mindset Horrortober Season 1, analyzes two films directed by their writers: Clive Barker's "Hellraiser" (1987) and William Peter Blatty's "The Exorcist III" (1990). The discussion, led by Brendan James, explores the contrasting visions of evil presented in these films, one from a British gay man and the other from a devout American Catholic. The podcast highlights the practical effects of "Hellraiser" and dissects a famous jump scare from "Exorcist III". The episode is available on the public feed after being previously released on Patreon.
Reference

Both films feature visions of Hell’s intrusion onto earth; two competing and complementary visions of evil, one from a gay British man and the second from a devout American Catholic.

The Schlapp's Exorcist (NVIDIA AI Podcast Episode Analysis)

Published:Sep 6, 2023 04:31
1 min read
NVIDIA AI Podcast

Analysis

This NVIDIA AI Podcast episode, titled "The Schlapp's Exorcist," presents a series of humorous and somewhat absurd rivalries. The episode's content, as described, covers a range of conflicts, from Elon Musk's rivalry with the ADL to the more abstract battles between men and houseplants, and even diarrhea and air travel. The podcast's focus seems to be on lighthearted commentary and potentially satirical takes on current events and societal trends, using the format of rivalries to explore these themes. The episode's title suggests a focus on the Schlapps and their involvement in a 'demonic possession' scenario, which adds a layer of intrigue.

Key Takeaways

Reference

The episode covers rivalries: Musk vs. the ADL, the Schlapps vs. Demonic possession, Men (all) vs. Houseplants, Diarrhea vs. Air Travel, and Techno-Libertarians vs. Mud.