Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned | Paper Citation Record

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This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Emerging Properties in Self-Supervised Vision Transformers

Reference 7

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Fairness in Machine Learning: A Survey

Reference 8

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 12

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned The Llama 3 Herd of Models

Reference 14

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Accessed: 2025-03-25

Reference 16

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Accessed: 2025- 03-25

Reference 18

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This paper cites Inherent Trade-Offs in the Fair Determination of Risk Scores.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Inherent Trade-Offs in the Fair Determination of Risk Scores

Reference 25

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This paper cites M6: A Chinese Multimodal Pretrainer.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned M6: A Chinese Multimodal Pretrainer

Reference 28

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Unresolved cited work

Reference 29

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This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 30

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This paper cites A Unified Approach to Interpreting Model Predictions.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned A Unified Approach to Interpreting Model Predictions

Reference 31

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned s1: Simple test-time scaling

Reference 34

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Functional trustworthiness of AI systems by statistically valid testing

Reference 35

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Parliament and the Council of the European Union

Reference 37

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Learning Transferable Visual Models From Natural Language Supervision

Reference 38

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 39

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Rajpurkar

Reference 40

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This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 41

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This paper cites "Why Should I Trust You?": Explaining the Predictions of Any Classifier.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned "Why Should I Trust You?": Explaining the Predictions of Any Classifier

Reference 42

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Label-wise Aleatoric and Epistemic Uncertainty Quantification

Reference 45

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This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 46

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned LaMDA: Language Models for Dialog Applications

Reference 48

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Tobin, R

Reference 49

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned LLaMA: Open and Efficient Foundation Language Models

Reference 50

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Unresolved cited work

Reference 51

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This paper cites Reinforcement Learning for Reasoning in Large Language Models with One Training Example.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 52

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications

Reference 54

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Florence: A New Foundation Model for Computer Vision

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned OPT: Open Pre-trained Transformer Language Models

Reference 56

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 57

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Weerts, R

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Conformity assessment – Vocabulary and general principles

Reference 1985

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 1986

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Unresolved cited work

Reference 1987

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned A Survey on Bias and Fairness in Machine Learning

Reference 1988

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 1990

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Learning Dexterous In-Hand Manipulation

Reference 1991

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Carlini and D

Reference 1995

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Training Compute-Optimal Large Language Models

Reference 1997

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Matejka and G

Reference 2005

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Reference 2006

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation

Reference 2007

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Second-Order Uncertainty Quantification: Variance-Based Measures

Reference 2009

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance

Reference 2012

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Explaining and Harnessing Adversarial Examples

Reference 2014

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

Reference 2015

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Riccio, F

Reference 2016

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned PaLM: Scaling Language Modeling with Pathways

Reference 2017

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Reference 2019

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Language Models are Few-Shot Learners

Reference 2020

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2021

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 2022

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Reference 2023

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned On the Opportunities and Risks of Foundation Models

Reference 2024

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Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Accessed: 2025-03-24

Reference 2025

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