Tezak, Nikolas

Evaluating Large Language Models Trained on Code

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv

Software Engineering ResearchNatural Language Processing Techniques

Cited by 1,436

Solving Rubik's Cube with a Robot Hand

OpenAI, Akkaya, I., Andrychowicz, M., Chociej, M., Litwin, M., McGrew, B., Petron, A., Paino, A., Plappert, M., Powell, G., et al. (2019) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 602

Unsupervised Machine Learning on a Hybrid Quantum Computer

Otterbach, J. S., Manenti, R., Alidoust, N., Bestwick, A., Block, M., Bloom, B., Caldwell, S., Didier, N., Fried, E. S., Hong, S., et al. (2017) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 263

OpenAI o1 System Card

OpenAI, :, Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 50

Efficient Training of Language Models to Fill in the Middle

Bavarian, M., Jun, H., Tezak, N., Schulman, J., McLeavey, C., Tworek, J., & Chen, M. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 47

A coherent perceptron for all-optical learning

Tezak, N., & Mabuchi, H. (2015) · EPJ Quantum Technology

Neural Networks and Reservoir ComputingSemiconductor Lasers and Optical Devices

Cited by 22