Scaling Method

Scaling methods encompass a broad range of techniques designed to improve the interpretability, efficiency, and robustness of various algorithms and models across diverse scientific domains. Current research focuses on developing robust scaling approaches for outlier detection, optimizing resource allocation in cloud computing (e.g., using hierarchical attention mechanisms and Bayesian decision-making), and enhancing the performance of neural networks (e.g., through variable scaling in physics-informed neural networks and improved attention mechanisms in transformers). These advancements are crucial for addressing challenges in areas such as data analysis, machine learning, and high-performance computing, leading to more accurate, efficient, and reliable results in a wide array of applications.

Papers

October 22, 2024

Using Platt's scaling for calibration after undersampling -- limitations and how to address them
Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford
Calibration Performance Fundamental Limitation Logistic Regression Multiplicative Size Scaling Model Calibration Additive Model Scaling Method Response Prediction

August 28, 2024

Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)
Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek, Franz Rothlauf
Quality Issue Outlier Detection Scaling Method Outlier Score

August 2, 2024

Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making
Yang Luo, Shiyu Wang, Zhemeng Yu, Wei Lu, Xiaofeng Gao, Lintao Ma, Guihai Chen
Resource Allocation Large Scale Datasets Large Scale Model Scaling Method Hierarchical Forecasting Bayesian Decision Resource Scaling Hierarchy Aware Attention

July 16, 2024

Quantum Maximum Entropy Inference and Hamiltonian Learning
Minbo Gao, Zhengfeng Ji, Fuchao Wei
Hamiltonian Learning Quasi Newton Method Quantum Generative Scaling Method

June 10, 2024

VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
Seungchan Ko, Sang Hyeon Park
Deep Neural Network Physic Informed Neural Network Neural Tangent Kernel Efficient Training Reg PINNs Scaling Method

May 3, 2024

Semantic Scaling: Bayesian Ideal Point Estimates with Large Language Models
Michael Burnham
Item Response Theory Research Survey Scaling Method Semantic Scale Prior Ideological Landscape Point Estimate

February 26, 2024

An inexact Bregman proximal point method and its acceleration version for unbalanced optimal transport
Xiang Chen, Faqiang Wang, Jun Liu, Li Cui
Proximal Point Unbalanced Optimal Transport Bregman Information Scaling Method Proximal Operator Bregman Proximal

November 15, 2023

Alternatives to the Scaled Dot Product for Attention in the Transformer Neural Network Architecture
James Bernhard
Human Attention Natural Gradient Multiplicative Size Scaling Dot Product Scaling Method Previously Generated Alternative Transformer Neural Network Architecture

October 18, 2023

Concept-Guided Chain-of-Thought Prompting for Pairwise Comparison Scaling of Texts with Large Language Models
Patrick Y. Wu, Jonathan Nagler, Joshua A. Tucker, Solomon Messing
Chain of Thought Text Based Generative Large Language Model Scaling Method Concept Explanation

October 7, 2023

The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning
Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite
Large Language Model Context Learning Context Example Hidden CoST Strong Scaling Scaling Method

September 19, 2023

PAMS: Platform for Artificial Market Simulations
Masanori Hirano, Ryosuke Takata, Kiyoshi Izumi
Deep Learning Diverse Platform Simulation Framework Price Prediction Scaling Method Market Simulation

April 28, 2023

Online Platt Scaling with Calibeating
Chirag Gupta, Aaditya Ramdas
Scaling Method Online Regression Post Hoc Calibration

January 19, 2023

Advanced Scaling Methods for VNF deployment with Reinforcement Learning
Namjin Seo, DongNyeong Heo, Heeyoul Choi
Reinforcement Learning Software Defined Scaling Method Network Function Network Paradigm

January 6, 2023

Myths and Legends in High-Performance Computing
Satoshi Matsuoka, Jens Domke, Mohamed Wahib, Aleksandr Drozd, Torsten Hoefler
High Performance Computing Novel Architecture Many Myth Scaling Method

December 23, 2022

The choice of scaling technique matters for classification performance
Lucas B. V. de Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz
Barzilai Borwein Technique Normalization Dictionary Value Laden Choice Classification Performance Scaling Method Data Scaling Optimal Scaling

September 16, 2022

Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
Boris Landa, Xiuyuan Cheng
Geometric Analysis Gaussian Kernel Scaling Method Robust Inference High Dimensional Noise

August 7, 2022

Stochastic Scaling in Loss Functions for Physics-Informed Neural Networks
Ethan Mills, Alexey Pozdnyakov
Neural Network Loss Function Physic Informed Neural Network Scaling Method Deep Learning Solution

August 1, 2022

Weighted Scaling Approach for Metabolomics Data Analysis
Biplab Biswas, Nishith Kumar, Md Aminul Hoque, Md Ashad Alam
Scaling Method Metabolomics Data

June 16, 2022

On Scaled Methods for Saddle Point Problems
Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev, Martin Takáč, Alexander Gasnikov
GAN Model GAN Training Saddle Point Learning Problem Scaling Method

March 20, 2022

Fully Convolutional Fractional Scaling
Michael Soloveitchik, Michael Werman
Convolutional Neural Network Scaling Method Fractional Order Gradient