Bileschi, Maxwell

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 537

Using deep learning to annotate the protein universe

Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J. (2022) · Nature Biotechnology

Machine Learning in BioinformaticsGenomics and Phylogenetic Studies

Cited by 332

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach

Jurenka, I., Kunesch, M., McKee, K. R., Gillick, D., Zhu, S., Wiltberger, S., Phal, S. M., Hermann, K., Kasenberg, D., Bhoopchand, A., et al. (2024) · arXiv

Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive Learning

Cited by 24

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Garcia, X., Liu, P. J., Harrison, J., Lee, J., Xu, K., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 10