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Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Learning word vectors for sentiment analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
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Ensemble modeling, uncertainty and robust predictions
Parker, W. S · 2013
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Findings of the 2014 workshop on statistical machine translation
Bojar, O., Buck, C., Federmann, C., Haddow, B., Koehn, P., Leveling, J., Monz, C., Pecina, P., Post, M., Saint-Amand, H., Soricut, R., Specia, L., and Tamchyna, A. s · 2014
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Densenet: Implementing efficient convnet descriptor pyramids
Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., and Keutzer, K · 2014
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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
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You are where you go: Inferring demographic attributes from location check-ins
Zhong, Y., Yuan, N. J., Zhong, W., Zhang, F., and Xie, X · 2015
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Professor forcing: A new algorithm for training recurrent networks
Lamb, A. M., Goyal, A. G. A. P., Zhang, Y., Zhang, S., Courville, A. C., and Bengio, Y · 2016
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Launch and iterate: Reducing prediction churn
Milani Fard, M., Cormier, Q., Canini, K., and Gupta, M · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Adaptive classification for prediction under a budget
Nan, F. and Saligrama, V · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A · 2017
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Ex machina: Personal attacks seen at scale
Wulczyn, E., Thain, N., and Dixon, L · 2017
Cited alongside, same era.
A call for clarity in reporting BLEU scores
Post, M · 2018
Cited alongside, same era.
On challenges in machine learning model management
Schelter, S., Biessmann, F., Januschowski, T., Salinas, D., Seufert, S., and Szarvas, G · 2018
Cited alongside, same era.
Idk cascades: Fast deep learning by learning not to overthink
Wang, X., Luo, Y., Crankshaw, D., Tumanov, A., Yu, F., and Gonzalez, J · 2018
Cited alongside, same era.
Why do larger models generalize better? a theoretical perspective via the xor problem
Brutzkus, A. and Globerson, A · 2019
Cited alongside, same era.
Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
Cotter, A., Jiang, H., Gupta, M. R., Wang, S., Narayan, T., You, S., and Sridharan, K · 2019
Locally adaptive label smoothing for predictive churn
Bahri, D. and Jiang, H · 2021
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Deep learning through the lens of example difficulty
Baldock, R. J. N., Maennel, H., and Neyshabur, B · 2021
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On the reproducibility of neural network predictions
Bhojanapalli, S., Wilber, K., Veit, A., Rawat, A. S., Kim, S., Menon, A., and Kumar, S · 2021
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Point-cloud based 3d object detection and classification methods for self-driving applications: A survey and taxonomy
Fernandes, D., Silva, A., Névoa, R., Simões, C., Gonzalez, D., Guevara, M., Novais, P., Monteiro, J., and Melo-Pinto, P · 2021
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Data integration using advances in machine learning in drug discovery and molecular biology
Hudson, I. L · 2021
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Cited alongside, same era.
An empirical study of example forgetting during deep neural network learning
Toneva, M., Sordoni, A., des Combes, R. T., Trischler, A., Bengio, Y., and Gordon, G. J · 2019
Cited alongside, same era.
Acl 2019 fourth conference on machine translation (wmt19), shared task: Machine translation of news
Wikimedia Foundation · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Cited alongside, same era.
Underspecification presents challenges for credibility in modern machine learning
D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al · 2020
Cited alongside, same era.
Calibration of pre-trained transformers
Desai, S. and Durrett, G · 2020
Cited alongside, same era.
Pretrained transformers improve out-of-distribution robustness
Hendrycks, D., Liu, X., Wallace, E., Dziedzic, A., Krishnan, R., and Song, D · 2020
Cited alongside, same era.
Bootstrapping for batch active sampling
Jiang, H. and Gupta, M. R · 2021
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Active testing: Sample-efficient model evaluation
Kossen, J., Farquhar, S., Gal, Y., and Rainforth, T · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A · 2021
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Analysis of weed growth in rabi crop agriculture using deep convolutional neural networks
Mishra, A. M., Shahare, Y., Gautam, V., et al · 2021
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Supervised transfer learning at scale for medical imaging
Mustafa, B., Loh, A., Freyberg, J., MacWilliams, P., Wilson, M., McKinney, S. M., Sieniek, M., Winkens, J., Liu, Y., Bui, P., et al · 2021
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When in doubt, summon the titans: Efficient inference with large models
Rawat, A. S., Zaheer, M., Menon, A. K., Ahmed, A., and Kumar, S · 2021
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isplinception: An inception-resnet deep learning architecture for human activity recognition
Ronald, M., Poulose, A., and Han, D. S · 2021
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The multiberts: Bert reproductions for robustness analysis
Sellam, T., Yadlowsky, S., Wei, J., Saphra, N., D’Amour, A., Linzen, T., Bastings, J., Turc, I., Eisenstein, J., Das, D., et al · 2021
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Are larger pretrained language models uniformly better? comparing performance at the instance level
Zhong, R., Ghosh, D., Klein, D., and Steinhardt, J · 2021
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