Mean Squared Error

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11 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Scale-Split Neural Operator for Memory- and Data-Efficient 3D Turbulence Prediction
Sep 30, 2026Shaoxiang Qin, Yucheng Zhao, Zongyi Li +2Turbulent Flows Neural Operators
University of Tennessee, Knoxville · McGill University · New York University +1
From HL to H+L-1 Parameters: A Hankel-Toeplitz Forecaster for Long-Term Time Series Forecasting
Sep 27, 2026Chaoqi Zhang, Yu Wang, Haixu TangMean Squared Error Long-Term
Indiana University Bloomington, IN, USA
Quantization Error Is Spectrally Flat: A Single Random Probe Is a Calibrated, Data-Free Sensitivity Estimator, with Application to Budget-Targeted Mixed-Precision Quantization
Sep 27, 2026I Kennedy, T KennedyMixed-Precision Quantization Mean Squared Error
baa.ai Auckland, New Zealand
TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements
Sep 26, 2026Saad Masrur, Saeed R. Khosravirad, Ismail GuvencDigital Twins Integrated Sensing And Communication
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC · Radio Systems Research, Nokia Bell Labs, Murray Hill, NJ, USA
Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction
Sep 23, 2026Ragamayi Puli, Shunya NagashimaRemote Photoplethysmography Time Series Forecasting
Neurogica Inc.
AnalogDepth: Multi-view Geometry from FPV drones under Analog Video Transmission
Sep 21, 2026André Amorim, Pedro F. ProençaDepth Estimation Dinov3
NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal.
Exposing Blind Spots in Deep Imbalanced Regression Evaluation
Sep 21, 2026Noah C. Puetz, Jens U. Brandt, Marc Hilbert +3Imbalanced Classification Mean Squared Error
TH Köln · Leiden University · Toyota Racing
GGUF-Metadata Prediction of Single-Sequence llama.cpp Throughput Across Three Systems
Sep 14, 2026Xinyu Qiu, Chuhong Xu, Bo Su +3Sequence Modeling Mean Squared Error
Northeastern University · Sofia University · Indiana University +2
EGGROLL, Unrolled: Understanding and Improving Low-Rank Evolution Strategies at Scale
Sep 12, 2026Ege C. Kaya, Abolfazl HashemiMean Squared Error Step-Level Advantage Estimation
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
When does a spectral prior help graph learning? Connectivity-loss estimation under road-network disruptions
Sep 11, 2026Van-Truong LeGraph Edge Sparsification Mean Squared Error
Faculty of Information Technology, University of Science, Viet Nam National University Ho Chi Minh City, Viet Nam
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Sep 11, 2026Bowen Zhang, Hsiu-Wen Cheng, Hongyu Yang +9Continuous Glucose Monitoring Diabetes
University of California, Los Angeles, Los Angeles, CA, U.S.A. · Union County Magnet High School, Scotch Plain, NJ, U.S.A. · Huntington Beach High School, Huntington Beach, CA, U.S.A. +3
Excitation-Supervised Closed-Loop Self-Calibration and Target Seeking for an Unknown-Pose Range-Bearing Relay
Aug 12, 2026Yash BaglaVisual Servoing Recalibration
Michigan State University
Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation
Aug 6, 2026Théo Danielou, Antoine Saporta, Léo Alberge +1Multimodal Pretraining Frozen Encoder
Raidium
Estimating SSIM from MSE for DCT-Based Compressed Images
Aug 3, 2026Luc Trudeau, Maria G. MartiniLearned Image Compression Image Quality Assessment
Universit´e du Qu´ebec `a Rimouski, Rimouski, QC, Canada · Kingston University London, London, UK
Start Classifying: Categorical Critics for LLM Reinforcement Learning
Aug 3, 2026Zhijian Zhou, Long Li, Xuan Zhang +7Large Language Model Reinforcement Learning Critic-Free Reinforcement Learning
Fudan University · Shanghai Innovation Institute · Tencent YoutuLab +2
Amortizing the Calibration Triple: A Projection-Consistent Neural Operator for Local-Stochastic Volatility
Aug 2, 2026Xiaozhen Wang, Anaïs Després, Martin Dureau +1Volatility Stochastic Differential Equations
1CEREMADE, Université Paris Dauphine-PSL · 2LaMME, Université Évry Paris-Saclay · 3AIML Global Markets, Barclays
Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods
Jul 21, 2026Seyed Mohammadreza Alavi, Ardeshir Shojaeinasab, Reza Jalayer +2Vibration Fault Diagnosis
Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran · Department of Electrical and Computer Engineering, University of Victoria, Victoria BC, Canada · FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, Korkeakoulunkatu 7, 33720 Tampere, Finland +1
Phasor Attention: Mean Root Square Normalization for Phase Manifold Preservation
Jul 20, 2026Sungwoo Goo, Hwi-yeol Yun, Sangkeun JungBatch Normalization Mean Squared Error
College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea · Department of Computer Science & Engineering, Chungnam National University, Daejeon, Republic of Korea
Decoder-Preserving Sparse Autoencoders: Which Readouts Survive Sparse Compression?
Jul 19, 2026Aniket DeshpandeImproving Sparse Autoencoders Reconstruction Error
Department of Physics, University of Illinois Urbana-Champaign
CanonicalPhys: Pose-Robust Remote Photoplethysmography via Canonical-Space Priors
Jul 17, 2026Hui Wei, Seyedata Jodeiri Seyedian, Xiaobai Li +1Remote Photoplethysmography Mean Squared Error
Center for Machine Vision and Signal Analysis (CMVS), University of Oulu · ELLIS Institute Finland · Zhejiang University
Clean2FX: Label-conditioned modeling for clean-to-effect guitar audio transformations
Jul 9, 2026Oliverio Bombicci Pontelli, Iran R. RomanAudio Editing Neural Audio
School of Electronic Engineering & Computer Science Queen Mary University of London London, United Kingdom
Critical Interval MSE: Toward Reliable Offline Validation for Robot Manipulation Policies
Jun 29, 2026Haoxu Huang, Tongsam Zheng, Yifan Chen +2Robot Policies Policy Evaluation
Tsinghua University · Shanghai Qi Zhi Institute · Spirit AI
RoPE-Aware Bit Allocation for KV-Cache Quantization
Jun 23, 2026Fengfeng Liang, Yuechen Zhang, Jiaya JiaKey-Value Cache Quantization Peak Memory
1Hong Kong University of Science and Technology · 2The Chinese University of Hong Kong · 3MiMo, Xiaomi Corporation
Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
Jun 20, 2026Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal +2Convex Loss Outliers
aSchool of Electrical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India · bErnst and Young GDS, Godrej Waterside, Ring Rd, DP Block, Sector V, Bidhannagar, Kolkata, 700 091, West Bengal, India · cSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India
Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization
Jun 7, 2026Patrik Czakó, Gábor Kertész, Sándor SzénásiLarge Language Model Quantization Mean Squared Error
Doctoral School of Applied Informatics and Applied Mathematics, Obuda University Budapest, Hungary · John von Neumann Faculty of Informatics, Obuda University Budapest, Hungary
PerBite: A Curated Diagnostic Workflow for Bite-Aware Food Volume Estimation
Jun 1, 2026Ahmad AlMughrabi, Farid Al-Areqi, David Fernández Gómez +4Diet Volume
1Universitat de Barcelona, Spain. · 4LogMeal, AIGecko Technologies S.L. · 2Grup de Tecnologies Interactives, Universitat Pompeu Fabra, Spain. +1
Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
May 28, 2026Gijs van Nieuwkoop, Siamak MehrkanoonPrecipitation Nowcasting Multi-Quantile Regression
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Minimax Limits of k-Fold Cross-Validation via Majority
May 25, 2026Ido Nachum, Rüdiger Urbanke, Thomas WeinbergerCross Validation Empirical Risk Minimization
University of Haifa, Haifa, Israel. · EPFL, Lausanne, Switzerland.
Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation
May 19, 2026Serhii ZabolotniiDensity Ratio Estimation Monte Carlo
Department of Information, Multimedia Technologies and Design, Cherkasy State Business College, Cherkasy, 18028, Ukraine · State Scientific Research Institute of Armament and Military Equipment Testing and Certification, Cherkasy, Ukraine · Department of Cybernetics and Applied Mathematics, Uzhhorod National University, Uzhhorod, Ukraine
Counting Machine Parts
May 18, 2026Benedict Florance Arockiaraj, Elizabeth Dinella, Ankit Billa +1Counting Mean Squared Error
Predicting missing values: A good idea?
May 5, 2026Stef van BuurenImputation Missing Data
TNO Netherlands Organization for Applied Scientific Research, Leiden · Dept. of Methodology & Statistics, University of Utrecht
HeadQ: Model-Visible Distortion and Score-Space Correction for KV-Cache Quantization
May 5, 2026Jorge L. Ruiz WilliamsKey-Value Cache Quantization Cache
Independent Researcher
A dimensional R2 regression metric
May 1, 2026Jaesung Yoo, Stefan Lemke, Jian Zhong Guo +2Intrinsic Dimensionality Kernel Ridge Regression
Neuroscience Center University of North Carolina at Chapel Hill Chapel Hill, NC, 27599 · Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Boston, MA, 02134
Learning Dynamic Evidence Routes for Vision Transformer Probing
Apr 29, 2026Zice Wang, Zhenyu ZhangLong Visual-Token Sequences Vision State Space Models
School of Future Technology Northeastern University Hunnan Campus, Shenyang, China
Training Time Prediction for Mixed Precision-based Distributed Training
Apr 17, 2026Minchul Kang, Changyong Shin, Jinwoo Jeong +5Training Time Full-Precision Performance
Korea University · KT Corporation
AEGIS: Anchor-Enforced Gradient Isolation for Knowledge-Preserving Vision-Language-Action Fine-Tuning
Apr 17, 2026Guransh SinghDiffusion-Based Vision-Language-Actions Knowledge-Based Visual Question Answering
Independent Researcher
Curvature-Aligned Probing for Local Loss-Landscape Stabilization
Apr 16, 2026Nikita Kiselev, Andrey GrabovoyAnisotropic Loss Landscapes Curvature-Aware Spectral Framework
Department of Intelligent Systems Moscow Institute of Physics and Technology
Hyperellipsoid Density Sampling: Exploitative Sequences to Accelerate High-Dimensional Numerical Optimization
Nov 11, 2025Julian G. SoltesMean Squared Error Exploitation
Regis University, Denver, Colorado, USA
SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction
Date pendingNahom BirhanInternet Of Thing Sensors
Department of Electrical and Computer Engineering Old Dominion University Virginia, USA

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

Scale-Split Neural Operator for Memory- and Data-Efficient 3D Turbulence Prediction

Shaoxiang Qin, Yucheng Zhao, Zongyi Li, Liangzhu Leon Wang+1

University of Tennessee, Knoxville · McGill University · New York University+1

Turbulent Flows Neural Operators Surrogate Models Wind Mean Squared Error

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Neural surrogates have emerged as fast alternatives to the numerical simulation of three-dimensional turbulence. However, training them at high resolution remains challenging, since the memory of full-field models grows with the resolution. In addition, full-resolution training data are expensive to simulate and store, and therefore scarce. We introduce ScaleSplit-NO (Scale-Split Neural Operator), which exploits the scale structure of turbulence with two neural operators: a Parent predicts the global coarse field at the next time step, and a Child predicts full-resolution local patches conditioned on this prediction. Neither model operates on the full-resolution field. The Child is pretrained alone and then attached to the Parent's coarse prediction through zero-initialized connections. On two complex high-resolution turbulence benchmarks, ScaleSplit-NO surpasses all competing baselines in both prediction accuracy and data efficiency. On the higher-resolution dataset JHTDB256 (

256^3

), its normalized mean squared error (NMSE) is 53% lower than that of the strongest baseline, and its training memory is 79% lower than that of the most memory-efficient baseline. We further demonstrate its effectiveness for urban wind prediction in a real district of Montreal on a

500\times150\times500

grid, reducing one-step NMSE by 65.8% relative to the baseline. Moreover, swapping in a Parent trained on additional coarse fields improves prediction without retraining the Child, providing further accuracy gains at a small storage cost.

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