Search:
Match:
3 results
Research Paper#Machine Learning, Generative Models, Score Matching🔬 ResearchAnalyzed: Jan 3, 2026 15:35

Improved Score Function Estimation and Hessian Estimation

Published:Dec 30, 2025 17:39
1 min read
ArXiv

Analysis

This paper investigates methods for estimating the score function (gradient of the log-density) of a data distribution, crucial for generative models like diffusion models. It combines implicit score matching and denoising score matching, demonstrating improved convergence rates and the ability to estimate log-density Hessians (second derivatives) without suffering from the curse of dimensionality. This is significant because accurate score function estimation is vital for the performance of generative models, and efficient Hessian estimation supports the convergence of ODE-based samplers used in these models.
Reference

“The paper demonstrates that implicit score matching achieves the same rates of convergence as denoising score matching and allows for Hessian estimation without the curse of dimensionality.”

PermalinkArXiv
Research#ViT🔬 ResearchAnalyzed: Jan 10, 2026 08:14

HEART-VIT: Optimizing Vision Transformers with Hessian-Guided Attention and Token Pruning

Published:Dec 23, 2025 07:23
1 min read
ArXiv

Analysis

This research explores optimization techniques for Vision Transformers (ViT) using Hessian-guided methods. The paper likely focuses on improving efficiency by reducing computational costs and memory requirements in ViT models.
Reference

“The paper introduces Hessian-Guided Efficient Dynamic Attention and Token Pruning in Vision Transformer (HEART-VIT).”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:22

Evolution Strategies

Published:Sep 5, 2019 00:00
1 min read
Lil'Log

Analysis

The article introduces black-box optimization algorithms as alternatives to stochastic gradient descent for optimizing deep learning models. It highlights the scenario where the target function's analytic form is unknown, making gradient-based methods infeasible. The article mentions examples like Simulated Annealing, Hill Climbing, and Nelder-Mead method, providing a basic overview of the topic.
Reference

“Stochastic gradient descent is a universal choice for optimizing deep learning models. However, it is not the only option. With black-box optimization algorithms, you can evaluate a target function $f(x): \mathbb{R}^n \to \mathbb{R}$, even when you don’t know the precise analytic form of $f(x)$ and thus cannot compute gradients or the Hessian matrix.”

PermalinkLil'Log