Calibration - Edgentiq

Improving Image Generation with Smarter Diffusion Model Guidance

TabTune: A Unified Framework for Tabular Foundation Models

LRT-Diffusion: Enhancing Offline RL with Calibrated Risk-Aware Guidance for Diffusion Policies

Navigating Uncertainty: How Partial Calibration Enhances Trustworthy AI Decisions

Deconstructing Performance Gaps in AI’s Selective Classifiers

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Recently Added

Enhancing Language Model Reasoning with Calibrated Sampling

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Challenging the Flat Minima Dogma: Why Sharper Solutions Can Lead to Better AI Performance

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Boosting AI’s Self-Assessment: How Finetuning Improves Language Model Uncertainty

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Generalized Models for Accurate LLM Correctness Prediction

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The Surprising Effects of Quantization on CLIP Model Performance and Reliability

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HALT-RAG: A Smart Approach to Verifying AI-Generated Content

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Making AI Predictions More Actionable for Humans

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Anti-Regularization: Boosting Model Performance in Data-Scarce Environments

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Enhancing Gaze Tracking on Phones Through Dynamic Calibration

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Addressing Overconfidence in AI Judges: New Metrics and Ensemble Approaches

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Enhancing Trustworthiness in Language Models: A Deep Dive into Calibration and Label Smoothing

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Gen AI News and Updates

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Improving Image Generation with Smarter Diffusion Model Guidance

November 11, 2025

TabTune: A Unified Framework for Tabular Foundation Models

November 5, 2025

LRT-Diffusion: Enhancing Offline RL with Calibrated Risk-Aware Guidance for Diffusion Policies

October 31, 2025