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DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization., , , , , , and . CoRR, (2023)Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities., , , , , , , , , and . CoRR, (2019)Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation., , , and . NAACL-HLT (1), page 3609-3619. Association for Computational Linguistics, (2019)On the Computational Power of Transformers and Its Implications in Sequence Modeling., , and . CoNLL, page 455-475. Association for Computational Linguistics, (2020)On the Ability of Self-Attention Networks to Recognize Counter Languages., , and . CoRR, (2020)On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages., , and . COLING, page 1481-1494. International Committee on Computational Linguistics, (2020)Structural Transfer Learning in NL-to-Bash Semantic Parsers., , and . CoRR, (2023)On the Ability and Limitations of Transformers to Recognize Formal Languages., , and . EMNLP (1), page 7096-7116. Association for Computational Linguistics, (2020)Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions., , , and . ACL (1), page 5767-5791. Association for Computational Linguistics, (2023)Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions., , , and . CoRR, (2023)