On Calibration of Modern Neural Networks
read the original abstract
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
This paper has not been read by Pith yet.
Forward citations
Cited by 47 Pith papers
-
Verifiable Rewards for Calibrated Probabilistic Forecasting
A verifiable empirical win rate reward combined with gradient masking enables RL training of a 7B model to reach betting-market calibration on NFL win probabilities using only outcome data.
-
Information Dynamics of Language Communication
The paper defines STE and SPID, two information-theoretic measures of semantic flow and decomposition in language exchanges, and applies them to four dialogue datasets.
-
Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
-
Bayesian Social Deduction with Graph-Informed Language Models
Hybrid Bayesian-graph LLM agent reaches competitive performance against large models and achieves 67% win rate against humans in controlled Avalon play, outperforming baselines and human teammates.
-
Expected Gain-based Escalation in Vertical Federated Learning
An analytical expected-gain score from calibrated posteriors and classwise reliability estimates decides escalation in VFL, improving communication-accuracy trade-off over baselines.
-
The Decomposition Is the Fingerprint: Per-Component Identity for Agent Skills
A per-component SimHash fingerprint supplies structural identity for AI agent skills, recovering family membership under paraphrase and refactoring with AUC 0.974 while localizing changes.
-
The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles
Empirical study on five datasets shows SMOTE causes minor ECE increase in tree ensembles while undersampling causes severe degradation with imbalance, and post-hoc recalibration restores calibration with negligible AUC loss.
-
Identifying Observational Signatures of Flux Eruption Events in Supermassive Black Hole Accretion Flows with Machine Learning
Machine learning on simulated images identifies that flux eruption events cause more diffuse, polarized, lower-flux millimeter emission with decreased Q-U loop rotation rate, achieving ~80% accuracy with random forest...
-
The Score Granularity Gap in Black-Box LLM Classification: A Comparative Study of Confidence Constructions
Comparative evaluation of seven confidence constructions across 25 LLM-dataset pairs reveals that verbalized scores provide good ranking but coarse granularity for thresholding, while multi-query aggregation helps wea...
-
Collaborative Large and Small Language Models for Accurate and Scalable Data Repair
LasRepair++ pairs an LLM instructor with an SLM corrector, refines context via EM, and down-weights uncertain repairs using column-calibrated confidence, reporting 18.1% average F1 gain over baselines on data repair tasks.
-
Beyond Explaining Predictions: Logic-Based Explanations for Confidence in Machine Learning Models
Defines MCT as the weakest confidence an abductive explanation can guarantee and proposes an optimization-based algorithm to generate minimal explanations meeting a target confidence threshold for boosted tree classifiers.
-
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
-
Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problems
Pointwise metrics compress marginal spectra in multimodal inverse problems, and a three-part protocol using CRPS, spectrum fidelity, and calibration reverses model rankings on synthetic and particle-physics benchmarks.
-
Training-Free Cultural Alignment of Large Language Models via Persona Disagreement
DISCA converts within-country disagreement among World Values Survey personas into a bounded logit correction that reduces cultural misalignment by 10-24% on MultiTP for models 3.8B and larger across 20 countries, wit...
-
Training-Free Cultural Alignment of Large Language Models via Persona Disagreement
DISCA uses disagreement among WVS-grounded persona panels to apply loss-averse logit corrections that reduce cultural misalignment by 10-24% on MultiTP for models 3.8B and larger, without weight changes.
-
Diversity in Large Language Models under Supervised Fine-Tuning
TOFU loss mitigates the narrowing of generative diversity in LLMs after supervised fine-tuning by addressing neglect of low-frequency patterns and forgetting of prior knowledge.
-
Pioneer Agent: Continual Improvement of Small Language Models in Production
Pioneer Agent automates the full lifecycle of adapting and continually improving small language models via diagnosis-driven data synthesis and regression-constrained retraining, delivering gains of 1.6-83.8 points on ...
-
Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification
Ensemble-based method of moments on softmax outputs produces stable Dirichlet predictive distributions that improve uncertainty-guided tasks like selective classification over evidential deep learning.
-
Photometric Redshift PDFs via Neural Network Classification for DESI Legacy Imaging Surveys and Pan-STARRS
Neural network classification with CRPS optimization produces calibrated photometric redshift PDFs for DESI Legacy and Pan-STARRS data, achieving σ_NMAD of 0.0153 on LSDR10 and outperforming regression methods.
-
Training Language Models to Self-Correct via Reinforcement Learning
SCoRe uses multi-turn online RL with regularization on self-generated traces to improve LLM self-correction, achieving 15.6% and 9.1% gains on MATH and HumanEval for Gemini models.
-
Language Models (Mostly) Know What They Know
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
-
Latent Confidence Alignment for LLM Self-Assessment
LCAE is introduced as a Rasch-model metric that aligns LLM self-reported confidence with latent error probability derived from ability and item difficulty, shown to improve calibration on a medical dataset across 20 models.
-
AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing
AURA is an adaptive uncertainty-aware refinement method for auditing LLM-as-a-judge pairwise decisions that learns human-consistency signals through selective human verification on uncertain cases.
-
Automated Essay Scoring and Language Certification: Assessing Generalizability, Agreement and Validity for French
Enhanced ABV framework applied to French AES, comparing 8 models on 27k and 961-essay corpora to assess generalizability, agreement, and validity.
-
TRACE: A taxonomy-grounded synthetic dataset for teaching-program generation and session interpretation in Applied Behavior Analysis
TRACE is a taxonomy-grounded synthetic instruction-tuning dataset with 2,999 examples for ABA teaching-program generation and multi-session behavioral interpretation, released with code, provenance, and stratified splits.
-
When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions
Introduces a unified framework integrating uncertainty estimation, calibration, and tool-based abstention for reliable code predictions in language models.
-
R2V Agent: Teaching SLMs When to Ask for Help
R2V-Agent combines an SLM policy trained via BC and DPO with a step-level risk-calibrated router using Brier scores and CVaR to escalate to LLM only on high residual failure risk, improving success-cost tradeoffs on H...
-
Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
Overthinking in medical QA is linearly decodable at 71.6% accuracy yet fixed residual-stream steering yields no correction across 29 configurations, while enabling selective abstention with AUROC 0.610.
-
Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction
Larger LLMs handle detailed crystal descriptions better than small ones, and mean negative log-likelihood of predicted numbers tracks prediction error after fine-tuning.
-
Diversity in Large Language Models under Supervised Fine-Tuning
Supervised fine-tuning narrows LLM generative diversity through neglect of low-frequency patterns and knowledge forgetting, but the TOFU loss mitigates this effect across models and benchmarks.
-
Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs
Sycophantic GRPO fine-tuning degrades LLM calibration, raising ECE by 0.006 and MCE by 0.010, with a persistent residual after post-hoc scaling.
-
MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification
MedFormer-UR integrates evidential uncertainty from Dirichlet distributions and class-specific prototypes into a transformer to improve calibration and selective prediction on medical images across four modalities.
-
Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review
PRISMA 2020 systematic review of 78 studies on fetal ultrasound plane classification paired with explainability or uncertainty, introducing the CALIB-XFUS reporting framework across six domains.
-
LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training
LiLAW learns to weight samples as easy, moderate or hard using three global scalars updated by one gradient step on a validation batch to improve noisy training performance.
-
Calibrated Model-Based Deep Reinforcement Learning
Augmenting model-based RL agents with calibrated predictive uncertainties improves planning, sample efficiency, and exploration on continuous control tasks.
-
Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
A Temporal Fusion Transformer with CORAL ordinal layer and autoregressive Mixture Density Network generates multi-horizon probabilistic trajectories and decomposed uncertainty estimates for Alzheimer's progression on ...
-
Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis
Foundation model representations from images and transcriptomics carry complementary signals for cancer classification; multimodal fusion improves results mainly when no modality dominates, and conformal prediction re...
-
Can Predicted Dynamics Exist in the Physical World?
Physical admissibility is defined as a prediction-control interface using kinematic, dynamic, and composed-horizon conditions to reject invalid dynamics proposals, with AUC 0.957 on LeRobot PushT and 87-89% prevention...
-
A Gaia-linked High-purity QSO Candidate Catalog in Selected Fields with Extinction-binned Calibration and Spectrum-informed Training
The P3 selector achieves 0.9809 purity and 0.8869 completeness for QSO candidates in selected fields, outperforming Gaia's official probabilities.
-
Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
A unified taxonomy of uncertainty in ML for physics is introduced together with validation tools such as coverage, calibration, and proper scoring rules, illustrated on regression and classification tasks.
-
TRACE: A Metrologically-Grounded Engineering Framework for Trustworthy Agentic AI Systems in Operationally Critical Domains
TRACE is a metrologically-grounded four-layer engineering framework for trustworthy agentic AI that enforces an ML-LLM split, stateful policies, human supervision, and a parsimony metric across critical domains.
-
Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models
DAVinCI combines claim attribution to model internals and external sources with entailment-based verification to improve LLM factual reliability by 5-20% on fact-checking datasets.
-
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems
Experiments show that shifted-ReLU layers can replace batch-normalization in single-bit-weight wide residual networks on CIFAR-10/100 and ImageNet without consistent accuracy penalty.
-
AI Assurance in UK Defence: Challenges in Operationalising JSP 936
A structured review of JSP 936 identifies eight challenge areas in operationalising AI assurance for UK Defence and concludes that further methods, guidance, and organisational capability are required.
-
Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study
CKD risk prediction models achieve AUROC 1.00 internally but drop to 0.48-0.58 externally with high calibration error and low deployment scores, indicating need for external validation.
-
Machine Learning Classification of Cryopathy Syndromes: A Comprehensive Comparative Study
Comparative study of machine learning models for multiclass classification of cryopathy syndromes from lab data, with best results from a soft-voting ensemble of Random Forest and Gradient Boosted Trees.
-
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
A survey of generative crystal modeling, multimodal learning, and closed-loop inverse design pipelines for crystalline solids, including failure modes and evaluation practices.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.