model performance

Coarse race data conceals disparities in clinical risk score performance MLHC 2023 GlobalBench: A Benchmark for Global Progress in Natural Language Processing EMNLP 2023 OpenDataVal: a Unified Benchmark for Data Valuation NeurIPS 2023 Revisiting Sample Size Determination in Natural Language Understanding ACL 2023 Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance EMNLP 2023 "Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts ICML 2023 A global analysis of metrics used for measuring performance in natural language processing ACL 2022 A survey on improving NLP models with human explanations ACL 2022 Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features ICLR 2022 FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning IJCAI 2022 Improving Bayesian Neural Networks by Adversarial Sampling AAAI 2022 Meaning Representations for Natural Languages: Design, Models and Applications EMNLP 2022 One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks EMNLP 2022 Towards a Rigorous Evaluation of Time-Series Anomaly Detection AAAI 2022 Fair Comparison: Quantifying Variance in Results for Fine-Grained Visual Categorization WACV 2021 Influence Selection for Active Learning ICCV 2021 Learning Prediction Intervals for Model Performance AAAI 2021 Mind the Performance Gap: Examining Dataset Shift During Prospective Validation MLHC 2021 Validating Label Consistency in NER Data Annotation EMNLP 2021 eTranslation’s Submissions to the WMT 2020 News Translation Task EMNLP 2020 Predicting Performance for Natural Language Processing Tasks ACL 2020 A Boo(n) for Evaluating Architecture Performance ICML 2018