Lanchantin, Jack

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 2,310

Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

Gao, J., Lanchantin, J., Soffa, M. L., & Qi, Y. (2018) · 2018 IEEE Security and Privacy Workshops (SPW)

Adversarial Robustness in Machine LearningSoftware Testing and Debugging Techniques

Cited by 693

Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data

Lanchantin, J., & Qi, Y. (2020) · Bioinformatics

Genomics and Chromatin DynamicsEpigenetics and DNA Methylation

Cited by 17

Learning to Reason and Memorize with Self-Notes

Lanchantin, J., Toshniwal, S., Weston, J., Szlam, A., & Sukhbaatar, S. (2023) · Advances in Neural Information Processing Systems 36

Innovative Teaching and Learning MethodsMemory Processes and Influences

Cited by 9

Diverse Preference Optimization

Lanchantin, J., Chen, A., Dhuliawala, S., Yu, P., Weston, J., Sukhbaatar, S., & Kulikov, I. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

Task-Dependent Evaluation of LLM Output Homogenization: A Taxonomy-Guided Framework

Jain, S., Lanchantin, J., Nickel, M., Ross, C., Ullrich, K., Wilson, A., & Watson-Daniels, J. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

Bridging Offline and Online Reinforcement Learning for LLMs

Lanchantin, J., Chen, A., Lan, J., Li, X., Saha, S., Wang, T., Xu, J., Yu, P., Yuan, W., Weston, J. E., et al. (2025) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 0