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Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.

G. Yu, S. Amizadeh, A. Pagnoni, B. Chun, M. Weimer, and M. Interlandi. CoRR, (2019)

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Analyzing Branch-and-Bound Algorithms for the Multiprocessor Scheduling Problem.T. Lively, W. Long, and A. Pagnoni. CoRR, (2019)Machine Learning at Microsoft with ML.NET.Z. Ahmed, S. Amizadeh, M. Bilenko, R. Carr, W. Chin, Y. Dekel, X. Dupré, V. Eksarevskiy, S. Filipi, T. Finley and 20 other author(s). KDD, page 2448-2458. ACM, (2019)Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics.A. Pagnoni, V. Balachandran, and Y. Tsvetkov. NAACL-HLT, page 4812-4829. Association for Computational Linguistics, (2021)Machine Learning at Microsoft with ML .NET.Z. Ahmed, S. Amizadeh, M. Bilenko, R. Carr, W. Chin, Y. Dekel, X. Dupré, V. Eksarevskiy, E. Erhardt, C. Eseanu and 24 other author(s). CoRR, (2019)Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization.A. Pagnoni, A. Fabbri, W. Kryscinski, and C. Wu. CoRR, (2022)Definition Frames: Using Definitions for Hybrid Concept Representations.E. Spiliopoulou, A. Pagnoni, and E. Hovy. COLING, page 3060-3068. International Committee on Computational Linguistics, (2020)PAC Learning Guarantees Under Covariate Shift.A. Pagnoni, S. Gramatovici, and S. Liu. CoRR, (2018)EvEntS ReaLM: Event Reasoning of Entity States via Language Models.E. Spiliopoulou, A. Pagnoni, Y. Bisk, and E. Hovy. EMNLP, page 1982-1997. Association for Computational Linguistics, (2022)StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization.V. Balachandran, A. Pagnoni, J. Lee, D. Rajagopal, J. Carbonell, and Y. Tsvetkov. CoRR, (2020)QLoRA: Efficient Finetuning of Quantized LLMs.T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer. CoRR, (2023)

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Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.

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Disambiguation of "Pagnoni, Artidoro"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.

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Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.