Graph-Based Continual Learning. - Binh Tang; David S. Matteson (ICLR, 2021)

Probabilistic Transformer For Time Series Analysis. - Binh Tang; David S. Matteson (NeurIPS, 2021)

PyXtal_FF: a python library for automated force field generation. - Howard Yanxon; David Zagaceta; Binh Tang; David S. Matteson; Qiang Zhu (Mach. Learn. Sci. Technol., 2021)

Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy. - Sreyas Mohan; Ramon Manzorro; Joshua L. Vincent; Binh Tang; Dev Yashpal Sheth; Eero P. Simoncelli; David S. Matteson; Peter A. Crozier; Carlos Fernandez-Granda (IEEE Trans. Computational Imaging, 2022)

A Theory on Adam Instability in Large-Scale Machine Learning. - Igor Molybog; Peter Albert; Moya Chen; Zachary DeVito; David Esiobu; Naman Goyal 001; Punit Singh Koura; Sharan Narang; Andrew Poulton; Ruan Silva; Binh Tang; Diana Liskovich; et al. (CoRR, 2023)

Llama 2: Open Foundation and Fine-Tuned Chat Models. - Hugo Touvron; Louis Martin; Kevin Stone; Peter Albert; Amjad Almahairi; Yasmine Babaei; Nikolay Bashlykov; Soumya Batra; Prajjwal Bhargava; Shruti Bhosale; Dan Bikel; Lukas Blecher; et al. (CoRR, 2023)

Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning. - Lili Yu; Bowen Shi; Ramakanth Pasunuru; Benjamin Muller; Olga Golovneva; Tianlu Wang; Arun Babu; Binh Tang; Brian Karrer; Shelly Sheynin; Candace Ross; Adam Polyak; et al. (CoRR, 2023)

The Llama 3 Herd of Models. - Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Ahmad Al-Dahle; Aiesha Letman; Akhil Mathur; Alan Schelten; Amy Yang; Angela Fan; Anirudh Goyal; Anthony Hartshorn; et al. (CoRR, 2024)

Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks. - Rylan Schaeffer; Punit Singh Koura; Binh Tang; Ranjan Subramanian; Aaditya K. Singh; Todor Mihaylov; Prajjwal Bhargava; Lovish Madaan; Niladri S. Chatterji; Vedanuj Goswami; Sergey Edunov; Dieuwke Hupkes; et al. (CoRR, 2025)