---
title: "Bryan Catanzaro — all works — Elamur Research"
description: "All 32 works of Bryan Catanzaro in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/bryan-catanzaro/works/"
---
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# Bryan Catanzaro — all works

32 works · [← Bryan Catanzaro](https://md.research.elamur.ai/free/en/authors/bryan-catanzaro/)

1. [**Deep Speech 2: End-to-End Speech Recognition in English and Mandarin arXiv:1512.02595**Dario Amodei, Rishita Anubhai, Eric Battenberg, et al.*2015 · 2,341 citations*](https://md.research.elamur.ai/free/en/papers/amodei-2015-deep-speech-2-end-end/)
2. [**Deep Speech: Scaling up end-to-end speech recognition arXiv:1412.5567**Awni Hannun, Carl Case, Jared Casper, et al.*2014 · 1,600 citations*](https://md.research.elamur.ai/free/en/papers/hannun-2014-deep-speech-scaling-up-end/)
3. **[cuDNN: Efficient Primitives for Deep Learning ↗](https://arxiv.org/abs/1410.0759) arXiv 2014 (NVIDIA) · no summary yet**Sharan Chetlur et al.*2014 · 1,240 citations*
4. **[WaveGlow: A Flow-based Generative Network for Speech Synthesis ↗](https://arxiv.org/abs/1811.00002) arXiv:1811.00002 · no summary yet**Ryan Prenger, Rafael Valle, Bryan Catanzaro*2018 · 1,055 citations*
5. **[Improving Semantic Segmentation via Video Propagation and Label Relaxation ↗](https://doi.org/10.1109/cvpr.2019.00906) IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yet**Yi Zhu et al.*2019 · 420 citations*
6. [**Reducing Activation Recomputation in Large Transformer Models arXiv:2205.05198**Vijay Korthikanti, Jared Casper, Sangkug Lym, et al.*2022 · 273 citations*](https://md.research.elamur.ai/free/en/papers/korthikanti-2022-reducing-activation-recomputation-large-transformer/)
7. **[Graphical Contrastive Losses for Scene Graph Parsing ↗](https://doi.org/10.1109/cvpr.2019.01180) IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yet**Ji Zhang et al.*2019 · 261 citations*
8. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models arXiv:2405.17428**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 222 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
9. **[Training Question Answering Models From Synthetic Data](https://md.research.elamur.ai/free/en/papers/puri-2020-training-question-answering-models-synthetic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Raul Puri, Ryan Spring, Mohammad Shoeybi, et al.*2020 · 208 citations*
10. **[Factuality Enhanced Language Models for Open-Ended Text Generation ↗](https://arxiv.org/abs/2206.04624) arXiv:2206.04624 · no summary yet**Nayeon Lee et al.*2022 · 168 citations*
11. **[MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://md.research.elamur.ai/free/en/papers/xu-2020-megatron-cntrl-controllable-story-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Peng Xu, Mostofa Patwary, Mohammad Shoeybi, et al.*2020 · 158 citations*
12. [**Retrieval meets Long Context Large Language Models arXiv:2310.03025**Peng Xu, Wei Ping, Xianchao Wu, et al.*2023 · 130 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-retrieval-meets-long-context-large/)
13. **[End-to-End Training of Neural Retrievers for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/sachan-2021-end-end-training-neural-retrievers/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Devendra Sachan, Mostofa Patwary, Mohammad Shoeybi, et al.*2021 · 116 citations*
14. [**Nemotron-4 340B Technical Report arXiv:2406.11704**Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/adler-2024-nemotron-4-340b-technical-report/)
15. **[An Empirical Study of Mamba-based Language Models ↗](https://arxiv.org/abs/2406.07887) arXiv:2406.07887 · no summary yet**Roger Waleffe et al.*2024 · 67 citations*
16. **[Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset](https://md.research.elamur.ai/free/en/papers/su-2025-nemotron-cc-transforming-common-crawl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Su, Kezhi Kong, Ying Lin, et al.*2025 · 65 citations*
17. **[ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://md.research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zihan Liu, Wei Ping, Rajarshi Roy, et al.*2024 · 58 citations*
18. **[Dual Contrastive Loss and Attention for GANs ↗](https://doi.org/10.1109/iccv48922.2021.00666) IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yet**Ning Yu et al.*2021 · 53 citations*
19. [**NVLM: Open Frontier-Class Multimodal LLMs arXiv:2409.11402**Wenliang Dai, Nayeon Lee, Boxin Wang, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/dai-2024-nvlm-open-frontier-class-multimodal/)
20. **[Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning ↗](https://aclanthology.org/2023.findings-emnlp.793/) Findings of the Association for Computational Linguistics: EMNLP 2023 · no summary yet**Zhuolin Yang et al.*2023 · 44 citations*
21. **[ODIN: Disentangled Reward Mitigates Hacking in RLHF ↗](https://arxiv.org/abs/2402.07319) arXiv:2402.07319 · no summary yet**Lichang Chen et al.*2024 · 38 citations*
22. **[AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling ↗](https://aclanthology.org/2025.findings-acl.206/) Findings of the Association for Computational Linguistics: ACL 2025 · no summary yet**Zihan Liu et al.*2025 · 37 citations*
23. [**Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models arXiv:2407.07263**Jupinder Parmar, Sanjev Satheesh, Mostofa Patwary, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/parmar-2024-reuse-don-t-retrain-recipe/)
24. **[AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning ↗](https://arxiv.org/abs/2505.16400) arXiv:2505.16400 · no summary yet**Yang Chen et al.*2025 · 30 citations*
25. [**AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy arXiv:2506.13284**Zihan Liu, Zhuolin Yang, Yang Chen, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-acereason-nemotron-1-1-advancing/)
26. **[MLSys: The New Frontier of Machine Learning Systems ↗](https://arxiv.org/abs/1904.03257) arXiv (Cornell University) · no summary yet**Alexander Ratner et al.*2019 · 19 citations*
27. [**Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining arXiv:2412.15285**Steven Feng, Shrimai Prabhumoye, Kezhi Kong, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/feng-2024-maximize-data-s-potential-enhancing/)
28. **[MIND: Math Informed syNthetic Dialogues for Pretraining LLMs ↗](https://arxiv.org/abs/2410.12881) arXiv:2410.12881 · no summary yet**Syeda Nahida Akter et al.*2024 · 11 citations*
29. **[Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning](https://md.research.elamur.ai/free/en/papers/akter-2026-nemotron-crossthink-scaling-self-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, et al.*2026 · 9 citations*
30. **[Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data ↗](https://arxiv.org/abs/2510.03264) arXiv:2510.03264 · no summary yet**Syeda Nahida Akter et al.*2025 · 6 citations*
31. **[From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-128k-4m-efficient-training-ultra/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chejian Xu, Wei Ping, Peng Xu, et al.*2026 · 5 citations*
32. **[Before They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models ↗](https://arxiv.org/abs/2610.10478) arXiv (Cornell University) · no summary yet**Tan Yu et al.*2026*