Wu, Carole-Jean

Deep Learning Recommendation Model for Personalization and Recommendation Systems

Naumov, M., Mudigere, D., Shi, H.-J. M., Huang, J., Sundaraman, N., Park, J., Wang, X., Gupta, U., Wu, C.-J., Azzolini, A. G., et al. (2019) · arXiv

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 411

Sustainable AI: Environmental Implications, Challenges and Opportunities

Wu, C.-J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., Chang, G., Behram, F. A., Huang, J., Bai, C., et al. (2021) · arXiv

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 405

MLPerf Training Benchmark

Mattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., Tang, H., Wei, G.-Y., Bailis, P., Bittorf, V., et al. (2019) · arXiv

Parallel Computing and Optimization TechniquesCloud Computing and Resource Management

Cited by 206

MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance

Mattson, P., Reddi, V. J., Cheng, C., Coleman, C., Diamos, G., Kanter, D., Micikevicius, P., Patterson, D., Schmuelling, G., Tang, H., et al. (2020) · IEEE Micro

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 171

Introducing v0.5 of the AI Safety Benchmark from MLCommons

Vidgen, B., Agrawal, A., Ahmed, A. M., Akinwande, V., Al-Nuaimi, N., Alfaraj, N., Alhajjar, E., Aroyo, L., Bavalatti, T., Bartolo, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 5