Results 11 to 20 of about 602,847 (266)

There is no generalizability crisis

Behavioral and Brain Sciences, 2021
AbstractFalsificationist and confirmationist approaches provide two well-established ways of evaluating generalizability. Yarkoni rejects both and invents a third approach we call neo-operationalism. His proposal cannot work for the hypothetical concepts psychologists use, because the universe of operationalizations is impossible to define, and ...
Daniel Lakens, Duygu Uygun Tunç, Mehmet Necip Tunç   +2 more
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The Generalizability of Explanations

2023 International Joint Conference on Neural Networks (IJCNN), 2023
Due to the absence of ground truth, objective evaluation of explainability methods is an essential research direction. So far, the vast majority of evaluations can be summarized into three categories, namely human evaluation, sensitivity testing, and salinity check.
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Assessing Generalizability of CodeBERT

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2021
Pre-trained models like BERT have achieved strong improvements on many natural language processing (NLP) tasks, showing their great generalizability. The success of pre-trained models in NLP inspires pre-trained models for programming language. Recently, CodeBERT, a model for both natural language (NL) and programming language (PL), pre-trained on code
Xin Zhou 0014, DongGyun Han, David Lo 0001   +2 more
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Generalizability of Subgroup Effects

Epidemiology, 2021
Generalizability methods are increasingly used to make inferences about the effect of interventions in target populations using a study sample. Most existing methods to generalize effects from sample to population rely on the assumption that subgroup-specific effects generalize directly.
Seamans, Marissa, Hong, Hwanhee, Ackerman, Benjamin, Schmid, Ian, Stuart, Elizabeth   +4 more
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One for More: Selecting Generalizable Samples for Generalizable ReID Model

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch. It will inevitably cause the model to over-fit the data in the dominant position (e.g., head data in imbalanced class, easy samples ...
Enwei Zhang, Xinyang Jiang, Hao Cheng 0012, Ancong Wu, Fufu Yu, Ke Li 0015, Xiaowei Guo, Feng Zheng 0001, Wei-Shi Zheng 0001, Xing Sun 0001   +9 more
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Data from Investigating Variation in Replicability: A “Many Labs” Replication Project

Journal of Open Psychology Data, 2014
This dataset is from the Many Labs Replication Project in which 13 effects were replicated across 36 samples and over 6,000 participants. Data from the replications are included, along with demographic variables about the participants and contextual ...
Richard A. Klein, Kate A Ratliff, Michelangelo Vianello, Reginald B Adams Jr., Stĕpán Bahník, Michael J Bernstein, Konrad Bocian, Mark J Brandt, Beach Brooks, Claudia Chloe Brumbaugh, Zeynep Cemalcilar, Jesse Chandler, Winnee Cheong, William E Davis, Thierry Devos, Matthew Eisner, Natalia Frankowska, David Furrow, Elisa Maria Galliani, Fred Hasselman, Joshua A Hicks, James F Hovermale, S Jane Hunt, Jeffrey R Huntsinger, Hans IJzerman, Melissa-Sue John, Jennifer A Joy-Gaba, Heather Barry Kappes, Lacy E Krueger, Jamie Kurtz, Carmel A Levitan, Robyn K Mallett, Wendy L Morris, Anthony J Nelson, Jason A Nier, Grant Packard, Ronaldo Pilati, Abraham M Rutchick, Kathleen Schmidt, Jeanine L Skorinko, Robert Smith, Troy G Steiner, Justin Storbeck, Lyn M Van Swol, Donna Thompson, A E van’t Veer, Leigh Ann Vaughn, Marek Vranka, Aaron L Wichman, Julie A Woodzicka, Brian A Nosek   +50 more
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The Generalizability Crisis

Behavioral and Brain Sciences, 2019
Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same
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Seminatural environments for rodent behavioral testing: a representative design improving animal welfare and enhancing replicability

Frontiers in Behavioral Neuroscience, 2023
The low replicability of scientific studies has become an important issue. One possible cause is low representativeness of the experimental design employed.
Enrique Hernández-Arteaga, Anders Ågmo
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Decoding Gestures in Electromyography: Spatiotemporal Graph Neural Networks for Generalizable and Interpretable Classification

IEEE Transactions on Neural Systems and Rehabilitation Engineering
In recent years, significant strides in deep learning have propelled the advancement of electromyography (EMG)-based upper-limb gesture recognition systems, yielding notable successes across a spectrum of domains, including rehabilitation, orthopedics ...
Hunmin Lee, Ming Jiang, Jinhui Yang, Zhi Yang, Qi Zhao   +4 more
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Replications, Comparisons, Sampling and the Problem of Representativeness in Animal Cognition Research

Animal Behavior and Cognition, 2021
Animal cognition research often involves small and idiosyncratic samples. This can constrain the generalizability and replicability of a study’s results and prevent meaningful comparisons between samples.
Benjamin G. Farrar, Konstantinos Voudouris, Nicola S. Clayton   +2 more
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