Bibliografía

Toda la bibliografía del facsímil, en APA 7

Las referencias de la sección «Para saber más» de cada capítulo, reunidas, ordenadas alfabéticamente y sin repeticiones: 932 obras en APA 7. Descárgalas en formato BibTeX para tu gestor de referencias (Zotero, Mendeley, LaTeX) o guarda esta página en PDF.

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  • Ba, J. L., Kiros, J. R. y Hinton, G. E. (2016). Layer normalization. arXiv:1607.06450. https://arxiv.org/abs/1607.06450
  • Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D. y Patel-Schneider, P. F. (eds.) (2003). The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press.
  • Baevski, A., Zhou, H., Mohamed, A. y Auli, M. (2020). wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. NeurIPS. https://arxiv.org/abs/2006.11477
  • Bagdasaryan, E., Hsieh, T.-Y., Nassi, B. y Shmatikov, V. (2023). (Ab)using Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs. https://arxiv.org/abs/2307.10490
  • Bahdanau, D., Cho, K. y Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. En International Conference on Learning Representations. https://arxiv.org/abs/1409.0473
  • Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., ... Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073. https://arxiv.org/abs/2212.08073
  • Bai, Y. y otros (2022). Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. https://arxiv.org/abs/2204.05862
  • Baltrušaitis, T., Ahuja, C. y Morency, L.-P. (2019). Multimodal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423-443. https://doi.org/10.1109/TPAMI.2018.2798607
  • Baltrušaitis, T., Ahuja, C. y Morency, L. P. (2019). Multimodal Machine Learning: A Survey and Taxonomy. IEEE TPAMI.
  • Bang, H. y Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973. https://doi.org/10.1111/j.1541-0420.2005.00377.x
  • Baye, C. A. (2016). Emergency Response. En Site Reliability Engineering. https://sre.google/sre-book/emergency-response/
  • Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370-418. https://doi.org/10.1098/rstl.1763.0053
  • Baylor, D. y otros (2017). TFX: A TensorFlow-Based Production-Scale Machine Learning Platform. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1387-1395. https://doi.org/10.1145/3097983.3098021
  • Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K. R. y Zhang, Y. (2019). AI Fairness 360: An Extensible Toolkit for Detecting and Mitigating Algorithmic Bias. IBM Journal of Research and Development, 63(4/5), 4:1-4:15. https://doi.org/10.1147/JRD.2019.2942287
  • Bellman, R. (1957). Dynamic programming. Princeton University Press.
  • Ben-Zaken, E., Ravfogel, S. y Goldberg, Y. (2022). BitFit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. Proceedings of ACL. https://arxiv.org/abs/2106.10199
  • Bender, E. M., Gebru, T., McMillan-Major, A. y Shmitchell, S. (2021). On the dangers of stochastic parrots: can language models be too big? En Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). https://doi.org/10.1145/3442188.3445922
  • Bengio, Y., Ducharme, R., Vincent, P. y Janvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137-1155. https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf
  • Benjamini, Y. y Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B, 57(1), 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
  • Berners-Lee, T. (2006). Linked Data. https://www.w3.org/DesignIssues/LinkedData.html
  • Bertasius, G., Wang, H. y Torresani, L. (2021). Is Space-Time Attention All You Need for Video Understanding? https://arxiv.org/abs/2102.05095
  • Bertsekas, D. P. (2012). Dynamic Programming and Optimal Control (Vol. 2, 4.ª ed.). Athena Scientific.
  • Beyer, B., Jones, C., Petoff, J. y Murphy, N. R. (eds.). (2016). Handling Overload. En Site Reliability Engineering. https://sre.google/sre-book/handling-overload/
  • Biere, A., Heule, M., van Maaren, H. y Walsh, T. (Eds.). (2009). Handbook of satisfiability. IOS Press.
  • Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
  • Bitol. (2026). Open Data Contract Standard. https://bitol-io.github.io/open-data-contract-standard/latest/
  • Blum, A. L. y Furst, M. L. (1997). Fast planning through planning graph analysis. Artificial Intelligence, 90(1-2), 281-300. https://doi.org/10.1016/S0004-3702(96)00047-1
  • Boardman, A. E., Greenberg, D. H., Vining, A. R. y Weimer, D. L. (2018). Cost-Benefit Analysis: Concepts and Practice (5.ª ed.). Cambridge University Press. https://doi.org/10.1017/9781108235594
  • Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V. y Kalai, A. T. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Advances in Neural Information Processing Systems 29, 4349-4357. https://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings
  • Bolya, D., Fu, C.-Y., Dai, X., Sun, P., Hoffman, J. y Feichtenhofer, C. (2023). Token Merging: Your ViT But Faster. ICLR 2023. https://arxiv.org/abs/2210.09461
  • Bondhugula, U., Hartono, A., Ramanujam, J. y Sadayappan, P. (2008). A practical automatic polyhedral parallelizer and locality optimizer. Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2008), 101-113. https://doi.org/10.1145/1375581.1375595
  • Bonet, B. y Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1-2), 5-33. https://doi.org/10.1016/S0004-3702(01)00108-4
  • Bonet, B. y Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1-2), 5-33.
  • Bradner, S. (1997). Key words for use in RFCs to Indicate Requirement Levels. RFC 2119. https://www.rfc-editor.org/rfc/rfc2119
  • Braintrust. (2026). Documentation. https://www.braintrust.dev/docs
  • Braintrust. (2026). Evaluate Systematically. https://www.braintrust.dev/docs/evaluate
  • Breck, E., Cai, S., Nielsen, E., Salib, M. y Sculley, D. (2017). The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction. IEEE Big Data, 1123-1132. https://research.google/pubs/pub46555/
  • Breck, E., Cai, S., Nielsen, E., Salib, M. y Sculley, D. (2017). The ML test score: A rubric for ML production readiness and technical debt reduction. 2017 IEEE International Conference on Big Data, 1123-1132. https://doi.org/10.1109/BigData.2017.8258038
  • Bredin, H. y otros (2019). pyannote.audio: neural building blocks for speaker diarization. https://arxiv.org/abs/1911.01255
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
  • Brewka, G., Dix, J. y Konolige, K. (1997). Nonmonotonic Reasoning: An Overview. CSLI Publications.
  • Brier, G. W. (1950). Verification of Forecasts Expressed in Terms of Probability. Monthly Weather Review, 78(1), 1-3. https://doi.org/10.1175/1520-0493(1950)078<0001:VOEPIO>2.0.CO;2
  • Broder, A. Z. (1997). On the Resemblance and Containment of Documents. Proceedings. Compression and Complexity of Sequences 1997, 21-29. https://doi.org/10.1109/SEQUEN.1997.666900
  • Brown, L. D., Cai, T. T. y DasGupta, A. (2001). Interval Estimation for a Binomial Proportion. Statistical Science, 16(2), 101-133. https://doi.org/10.1214/ss/1009213286
  • Brown, T. B. y otros (2020). Language models are few-shot learners. En Advances in Neural Information Processing Systems 33 (pp. 1877-1901). https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
  • Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S. y Colton, S. (2012). A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), 1-43. https://doi.org/10.1109/TCIAIG.2012.2186810
  • Bubeck, S. y Cesa-Bianchi, N. (2012). Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends in Machine Learning, 5(1), 1-122. https://doi.org/10.1561/2200000024
  • Buch, S., Eyzaguirre, C., Gaidon, A., Wu, J., Fei-Fei, L. y Carlos Niebles, J. (2022). Revisiting the "Video" in Video-Language Understanding. CVPR 2022. https://arxiv.org/abs/2206.01720
  • Buchanan, B. G. y Shortliffe, E. H. (1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley.
  • Buolamwini, J. y Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 77-91. https://proceedings.mlr.press/v81/buolamwini18a.html
  • Bycroft, B. (2023). LLM Visualization. https://bbycroft.net/llm
  • Bylander, T. (1994). The computational complexity of propositional STRIPS planning. Artificial Intelligence, 69(1-2), 165-204. https://doi.org/10.1016/0004-3702(94)90081-7

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  • Yan, S.-Q., Gu, J.-C., Zhu, Y. y Ling, Z.-H. (2024). Corrective Retrieval Augmented Generation. https://arxiv.org/abs/2401.15884
  • Yang, A. y otros (2024). Qwen2.5 technical report. https://arxiv.org/abs/2412.15115
  • Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D. y Chen, X. (2023). Large Language Models as Optimizers. https://arxiv.org/abs/2309.03409
  • Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y. y Narasimhan, K. (2023). Tree of thoughts: deliberate problem solving with large language models. https://arxiv.org/abs/2305.10601
  • Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K. y Cao, Y. (2023). ReAct: synergizing reasoning and acting in language models. International Conference on Learning Representations. https://arxiv.org/abs/2210.03629
  • Yu, H. y otros (2024). Evaluation of Retrieval-Augmented Generation: A Survey. https://arxiv.org/abs/2405.07437
  • Yu, T. y otros (2018). Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. Proceedings of EMNLP, 3911-3921. https://aclanthology.org/D18-1425/
  • Yue, X. y otros (2024). MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://arxiv.org/abs/2311.16502

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