Preprint2022
Autodeuq: Automated deep ensemble with uncertainty quantification
Unknown
AutoDEUQ automates deep ensemble construction with uncertainty quantification, evaluated on 10 regression benchmark datasets.
0Jan 1, 2022Benchmarks
AI Research
AI & ML Research Papers
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
All Papers
13 papersPreprint2026
CLIP-CC-Bench: Evaluating Paragraph-Level Video Descriptions in Video-Language Models
Mukhtiar Ali, Harsh Dubey, Sugam Mishra, et al.
Introduces CLIP-CC-Bench, an evaluation suite for long-form video description using 90-second movie clips and expert-written paragraph references, with an ensemble of five LLM embeddings and coarse/fine-grained semantic matching to rank 17 video-lang
0Aug 5, 2026MultimodalBenchmarks
arXiv
JournalSN Applied Sciences2023
Early warning of low visibility using the ensembling of machine learning approaches for aviation services at Jay Prakash Narayan International (JPNI) Airport Patna
Anand Shankar, Bikash Chandra Sahana
This paper proposes an ensemble of machine learning approaches (boosting, bagging, stacking) to predict low visibility and dense fog at Patna airport, achieving high reliability for aviation services.
21Apr 7, 2023
Preprint2018
Knowledge distillation by on-the-fly native ensemble
Unknown
Proposes a novel online knowledge distillation method that is more efficient and effective than existing approaches.
0Jan 1, 2018Knowledge Distillation
Preprintnpj Artificial Intelligence2026
AIFS-CRPS: ensemble forecasting using a model trained with a loss function based on the continuous ranked probability score
Simon Lang, Mihai Alexe, Mariana C. A. Clare, et al.
Introduces AIFS-CRPS, an ensemble weather forecasting variant of ECMWF's AIFS, trained with an almost fair CRPS loss, outperforming the physics-based IFS ensemble for most variables and lead times.
109Feb 2, 2026
arXiv
Preprint2026
Aurora: Automated training framework of universal process reward models via ensemble prompting and reverse verification
Unknown
Aurora is an automated framework for training universal process reward models using ensemble prompting and reverse verification.
0Jan 1, 2026Prompting
Preprint2026
OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis
Zihan Li, Feiyang Liu, Dandan Shan, et al.
OPERA is a multi-agent ensemble framework that treats expert weight assignment as offline policy learning to enable deployable biomedical AI without retraining.
0Jul 27, 2026Fine TuningMultimodal
arXiv
JournalIEEE Transactions on Information Forensics and Security2012
Rich Models for Steganalysis of Digital Images
Jessica Fridrich, Jan Kodovský
This paper introduces a novel strategy for building steganalysis detectors by assembling a rich model of noise residuals and using ensemble classifiers, achieving high detection accuracy against spatial-domain steganography.
2.1kMay 24, 2012Quantization
Preprint2012
Ensemble Methods: Foundations and Algorithms
Zhi‐Hua Zhou
A comprehensive textbook covering ensemble methods including Boosting, Bagging, Random Forest, and diversity measures.
2.1kJan 1, 2012
JournalIEEE Circuits and Systems Magazine2006
Ensemble based systems in decision making
Robi Polikar
This paper reviews ensemble-based systems in decision making, covering conditions for their superiority over single classifiers, algorithms like bagging and boosting, combination rules, and future applications.
2.9kJan 1, 2006
ConferenceAAAI Conference on Artificial Intelligence2017Featured
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, et al.
This paper demonstrates that residual connections accelerate Inception network training and improve accuracy, achieving 3.08% top-5 error on ImageNet with an ensemble.
16kFeb 12, 2017Scaling Laws
arXiv
PreprintInformation Fusion2021
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, et al.
Comprehensive review of uncertainty quantification methods in deep learning, covering Bayesian and ensemble techniques across vision, NLP, and reinforcement learning.
2.7kMay 23, 2021Reinforcement LearningComputer Vision
arXiv