Articles for category: AI Research

March 9, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

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March 9, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 9, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 9, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 9, 2025

ikayaniaamirshahzad@gmail.com

Reconstructing 3D objects from images with unknown poses

We leverage two key techniques to aid convergence of this ill-posed problem. The first is a very lightweight, dynamically trained convolutional neural network (CNN) encoder that regresses camera poses from training images. We pass a downscaled training image to a four layer CNN that infers the camera pose. This CNN is initialized from noise and requires no pre-training. Its capacity is so small that it forces similar looking images to similar poses, providing an implicit regularization greatly aiding convergence. The second technique is a modulo loss that simultaneously considers pseudo symmetries of an object. We render the object from a

March 9, 2025

ikayaniaamirshahzad@gmail.com

Microsoft Research and Physics Wallah team up to enhance AI-based tutoring

In India, limited resources, geographical constraints, and economic factors present barriers to quality education for some students. A shortage of teachers, particularly in remote or low-income areas, makes it harder for students to receive the guidance they need to prepare for highly competitive professional and academic programs. Microsoft Research is developing new algorithms and techniques that are enabling Physics Wallah (opens in new tab), a growing educational company, to make its AI-based tutoring services more accurate and reliable, to better support students on their education journey. As in other countries, many Indian students purchase coaching and tutoring services to prepare for

March 9, 2025

ikayaniaamirshahzad@gmail.com

Branch Specialization

This article is part of the Circuits thread, an experimental format collecting invited short articles and critical commentary delving into the inner workings of neural networks. Visualizing Weights Weight Banding Introduction If we think of interpretability as a kind of “anatomy of neural networks,” most of the circuits thread has involved studying tiny little veins – looking at the small-scale, at individual neurons and how they connect. However, there are many natural questions that the small-scale approach doesn’t address. In contrast, the most prominent abstractions in biological anatomy involve larger-scale structures: individual organs like the heart, or entire organ systems

March 9, 2025

ikayaniaamirshahzad@gmail.com

Error estimation and adaptive tuning for unregularized robust M-estimator

Error estimation and adaptive tuning for unregularized robust M-estimator Pierre C. Bellec, Takuya Koriyama; 26(16):1−40, 2025. Abstract We consider unregularized robust M-estimators for linear models under Gaussian design and heavy-tailed noise, in the proportional asymptotics regime where the sample size n and the number of features p are both increasing such that $p/n \to \gamma\in (0,1)$. An estimator of the out-of-sample error of a robust M-estimator is analyzed and proved to be consistent for a large family of loss functions that includes the Huber loss. As an application of this result, we propose an adaptive tuning procedure of the scale

March 9, 2025

ikayaniaamirshahzad@gmail.com

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer

Ethics statement Our research complies with all relevant ethical regulations. The study protocol was approved by Applied Bioinformatics Laboratories of New York University Grossman School of Medicine and the Department of Surgery of Leiden University Medical Center. The analyses were performed using anonymized archival material, not necessitating additional informed consents. Data from TCGA-COAD was open-accessed, ensuring patient anonymity without risk of patient identification. All institutions contributing annotated biospecimens provided documentation to the TCGA, and have obtained ethical approvals to use the sample and data according to the human subjects protection and data access policies in TCGA program. Archival material derived

March 9, 2025

ikayaniaamirshahzad@gmail.com

The Practitioner’s Guide to the Maximal Update Parameterization

EleutherAI is proud to introduce a joint project with Cerebras on spreading the implementation details of muTransfer with the wider model training community! We provide a simple port of μP to the popular nanoGPT library at https://github.com/EleutherAI/nanoGPT-mup, and encourage readers to refer to this implementation throughout this blog. Maximal Update Parameterization (μP) offers significant advantages for neural network training, but its adoption has been limited due to the complexity of the underlying math and the challenges in implementation. This guide aims to lower those barriers by providing a clear and practical overview of μP. By using μP, you can achieve