Georgiev, Petko

Grandmaster level in StarCraft II using multi-agent reinforcement learning

Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,455

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., & Kawsar, F. (2016) · 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 450

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Can Deep Learning Revolutionize Mobile Sensing?

Lane, N. D., & Georgiev, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 282

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 149

Low-resource multi-task audio sensing for mobile and embedded devices via shared deep neural network representations

Georgiev, P., Bhattacharya, S., Lane, N. D., & Mascolo, C. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

Music and Audio ProcessingSpeech Recognition and Synthesis

Cited by 67

LEO: scheduling sensor inference algorithms across heterogeneous mobile processors and network resources

Georgiev, P., Lane, N. D., Rachuri, K. K., & Mascolo, C. (2016) · Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking

IoT and Edge/Fog ComputingMobile and Web Applications

Cited by 58

Imitating Interactive Intelligence

Abramson, J., Ahuja, A., Barr, I., Brussee, A., Carnevale, F., Cassin, M., Chhaparia, R., Clark, S., Damoc, B., Dudzik, A., et al. (2020) · arXiv

Reinforcement Learning in RoboticsSocial Robot Interaction and HRI

Cited by 43