A 65nm CMOS self-terminated open-drain IDAC line driver suitable for fast Ethernet applications. - Joseph N. Y. Aziz; Ark-Chew Wong; Andrew Chen; Derek Tam (CICC, 2011)

8.6 A full-duplex line driver for Gigabit Ethernet with rail-to-rail class-AB output stage in 28nm CMOS. - Hui Pan 002; Yuan Yao; Mostafa Hammad; Junhua Tan; Karim Abdelhalim; Evelyn Wenting Wang; Rick C. J. Hsu; Jenny Yu; Joseph N. Y. Aziz; Derek Tam; Ichiro Fujimori (ISSCC, 2014)

A Full-Duplex Line Driver for Gigabit Ethernet With Rail-to-Rail Class-AB Output Stage in 28 nm CMOS. - Hui Pan 002; Yuan Yao; Mostafa Hammad; Junhua Tan; Karim Abdelhalim; Evelyn Wenting Wang; Rick C. J. Hsu; Derek Tam; Ichiro Fujimori (IEEE J. Solid State Circuits, 2014)

10.3 An analog front-end for 100BASE-T1 automotive Ethernet in 28nm CMOS. - Hui Pan 002; Junhua Tan; Evelyn Wenting Wang; Jingguang Wang; Karthik Swaminathan; Ramalingam Pandarinathan; Ramesh Pasagadugula; VamshiKrishna Yakkala; Mostafa Hammad; Karim Abdelhalim; Kaijun Li; Su Cui; et al. (ISSCC, 2016)

Optimal Transport-based Alignment of Learned Character Representations for String Similarity. - Derek Tam; Nicholas Monath; Ari Kobren; Aaron Traylor; Rajarshi Das; Andrew McCallum (ACL (1), 2019)

Predicting Institution Hierarchies with Set-based Models. - Derek Tam; Nicholas Monath; Ari Kobren; Andrew McCallum (AKBC, 2020)

Improving and Simplifying Pattern Exploiting Training. - Derek Tam; Rakesh R. Menon; Mohit Bansal; Shashank Srivastava; Colin Raffel (EMNLP (1), 2021)

Prosody-Aware Neural Machine Translation for Dubbing. - Derek Tam; Surafel Melaku Lakew; Yogesh Virkar; Prashant Mathur; Marcello Federico (CoRR, 2021)

Isochrony-Aware Neural Machine Translation for Automatic Dubbing. - Derek Tam; Surafel Melaku Lakew; Yogesh Virkar; Prashant Mathur; Marcello Federico (INTERSPEECH, 2022)

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. - Haokun Liu; Derek Tam; Mohammed Muqeeth; Jay Mohta; Tenghao Huang; Mohit Bansal; Colin Raffel (NeurIPS, 2022)

Evaluating the Factual Consistency of Large Language Models Through Summarization. - Derek Tam; Anisha Mascarenhas; Shiyue Zhang 001; Sarah Kwan; Mohit Bansal; Colin Raffel (CoRR, 2022)

Evaluating the Factual Consistency of Large Language Models Through News Summarization. - Derek Tam; Anisha Mascarenhas; Shiyue Zhang 001; Sarah Kwan; Mohit Bansal; Colin Raffel (ACL (Findings), 2023)

TIES-Merging: Resolving Interference When Merging Models. - Prateek Yadav; Derek Tam; Leshem Choshen; Colin A. Raffel; Mohit Bansal (NeurIPS, 2023)

Resolving Interference When Merging Models. - Prateek Yadav; Derek Tam; Leshem Choshen; Colin Raffel; Mohit Bansal (CoRR, 2023)

Merging by Matching Models in Task Subspaces. - Derek Tam; Mohit Bansal; Colin Raffel (CoRR, 2023)

An Empirical Survey of Data Augmentation for Limited Data Learning in NLP. - Jiaao Chen; Derek Tam; Colin Raffel; Mohit Bansal; Diyi Yang (Trans. Assoc. Comput. Linguistics, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Realistic Evaluation of Model Merging for Compositional Generalization. - Derek Tam; Yash Kant; Brian Lester; Igor Gilitschenski; Colin Raffel (CoRR, 2024)

Merging by Matching Models in Task Parameter Subspaces. - Derek Tam; Mohit Bansal; Colin Raffel (Trans. Mach. Learn. Res., 2024)

Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator. - YuXin Li; Felix Dangel; Derek Tam; Colin Raffel (CoRR, 2025)