Recoverable Identifier
advisory
doi_compliance
recoverable_identifier
DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/SP46214.2022.9833647.URL) was visible in the surrounding text but could not be confirmed against doi.org as printed.
Paper page Integrity report arXiv Try DOI
Evidence text
ISBN 978-1-66541-316-9. doi: 10.1109/SP46214. 2022.9833647. URL https://ieeexplore.ieee. org/document/9833647/. Place: San Francisco, CA, USA. Spoerer, C. J., Kietzmann, T. C., Mehrer, J., Charest, I., and Kriegeskorte, N. Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision.PLOS Computational Biology, 16 (10):e1008215, October 2020. ISSN 1553-7358. doi: 10.1371/journal.pcbi.1008215. Sutor, P., Yuan, D., Summers-Stay, D., Ferm¨uller, C., and Aloimonos, Y . Gluing neural networks symbolically through hyperdimensional computing. InProceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1–10, 2022. doi: 10.1109/IJCNN55064. 2022.9892461. Sutton, R. The bitter lesson.Incomplete Ideas (blog), 13(1): 38, 2019. Sutton, R. S. and Barto, A. G.Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learn- ing Series. The MIT Press, Cambridge, Massachusetts, second edition edition, 2018. ISBN 978-0-262-03924-6. Tankelevitch, L., Kewenig, V ., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., and Rintel, S. The metacog- nitive demands and opportunities of generative ai. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, CHI ’24, New York, NY , USA, 2024. Association for Computing Machin- ery. ISBN 9798400703300. doi: 10.1145/3613904. 3642902. URL https://doi.org/10.1145/ 3613904.3642902. Taylor, B., Marco, V . S., Wolff, W., Elkhatib, Y ., and Wang, Z. Adaptive deep lear
Evidence payload
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