:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kim, Mijoo, Kwon, Junseok
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2407.12330
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
by: Lee, Jung Hyun, et al.
Published: (2023)

Reconsidering LLM Uncertainty Estimation Methods in the Wild
by: Bakman, Yavuz, et al.
Published: (2025)

Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning
by: Ye, Tiandi, et al.
Published: (2025)

Integrated Gradient Correlation: a Dataset-wise Attribution Method
by: Lelièvre, Pierre, et al.
Published: (2024)

Calibrated Explanations: with Uncertainty Information and Counterfactuals
by: Lofstrom, Helena, et al.
Published: (2023)

DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment
by: Kwon, Sangwoo, et al.
Published: (2025)

Disentangling Hyperedges through the Lens of Category Theory
by: Lee, Yoonho, et al.
Published: (2025)

Datum-wise Transformer for Synthetic Tabular Data Detection in the Wild
by: Kindji, G. Charbel N., et al.
Published: (2025)

Better Not to Propagate: Understanding Edge Uncertainty and Over-smoothing in Signed Graph Neural Networks
by: Choi, Yoonhyuk, et al.
Published: (2024)

A Predictive Model Based on Transformer with Statistical Feature Embedding in Manufacturing Sensor Dataset
by: Lee, Gyeong Taek, et al.
Published: (2024)

Adjusting Regression Models for Conditional Uncertainty Calibration
by: Gao, Ruijiang, et al.
Published: (2024)

From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
by: Jenane, Azza, et al.
Published: (2026)

AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler
by: Kim, Changhun, et al.
Published: (2024)

Two-Stage Grid Optimization for Group-wise Quantization of LLMs
by: Kim, Junhan, et al.
Published: (2026)

ConformaDecompose: Explaining Uncertainty via Calibration Localization
by: Yapicioglu, Fatima Rabia, et al.
Published: (2026)

Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision
by: Valencia, Kevin, et al.
Published: (2026)

Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression
by: Kwon, Taehyung, et al.
Published: (2026)

SpecBound: Adaptive Bounded Self-Speculation with Layer-wise Confidence Calibration
by: Wen, Zhuofan, et al.
Published: (2026)

Not All Instances Are Equally Valuable: Towards Influence-Weighted Dataset Distillation
by: Deng, Qiyan, et al.
Published: (2025)

Calibrated Dataset Condensation for Faster Hyperparameter Search
by: Ding, Mucong, et al.
Published: (2024)

Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior
by: Gros, Timo P., et al.
Published: (2025)

Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration
by: Cheng, Chih-Hong, et al.
Published: (2024)

Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration
by: Decker, Thomas, et al.
Published: (2025)

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations
by: Decker, Thomas, et al.
Published: (2025)

Calibrated Physics-Informed Uncertainty Quantification
by: Gopakumar, Vignesh, et al.
Published: (2025)

Insect Identification in the Wild: The AMI Dataset
by: Jain, Aditya, et al.
Published: (2024)

MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising
by: Dai, Quanyu, et al.
Published: (2025)

Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning
by: Manchingal, Shireen Kudukkil, et al.
Published: (2026)

Energy-Efficient Wireless LLM Inference via Uncertainty and Importance-Aware Speculative Decoding
by: Park, Jihoon, et al.
Published: (2025)

Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
by: Stutts, Alex Christopher, et al.
Published: (2024)

A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
by: Yang, Qidong, et al.
Published: (2024)

Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
by: Kamarthi, Harshavardhan, et al.
Published: (2024)

CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment
by: Mendes, Pedro, et al.
Published: (2025)

Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation
by: Radharapu, Bhaktipriya, et al.
Published: (2025)

Structured Matrix Scaling for Multi-Class Calibration
by: Berta, Eugène, et al.
Published: (2025)

Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets
by: Chen, Kaiyuan, et al.
Published: (2025)

Towards LLM-Centric Multimodal Fusion: A Survey on Integration Strategies and Techniques
by: An, Jisu, et al.
Published: (2025)

Subgraph Federated Learning for Local Generalization
by: Kim, Sungwon, et al.
Published: (2025)

Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025)

Diffusion Tensor Estimation with Uncertainty Calibration
by: Karimi, Davood, et al.
Published: (2021)