Cox, David

Visual Place Recognition: A Survey

Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016) · IEEE Transactions on Robotics

Robotics and Sensor-Based LocalizationAdvanced Image and Video Retrieval Techniques

Cited by 1,079

Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms

Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S., et al. (2020) · JAMA Network Open

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 409

Triton: an intermediate language and compiler for tiled neural network computations

Tillet, P., Kung, H. T., & Cox, D. (2019) · Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages

Parallel Computing and Optimization TechniquesEmbedded Systems Design Techniques

Cited by 318

A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

Pinto, N., Doukhan, D., DiCarlo, J. J., & Cox, D. D. (2009) · PLoS Computational Biology

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 248

A neural network trained for prediction mimics diverse features of biological neurons and perception

Lotter, W., Kreiman, G., & Cox, D. (2020) · Nature Machine Intelligence

Visual perception and processing mechanismsNeural dynamics and brain function

Cited by 128

Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

Sun, Z., Shen, Y., Zhou, Q., Zhang, H., Chen, Z., Cox, D., Yang, Y., & Gan, C. (2023) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 81

Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks

Abdelaziz, I., Basu, K., Agarwal, M., Kumaravel, S., Stallone, M., Panda, R., Rizk, Y., Bhargav, G. P. S., Crouse, M., Gunasekara, C., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track

Topic ModelingNatural Language Processing Techniques

Cited by 22