Publications

Search
Author Title Type Year
showcase
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
reinforcement learning
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
year-in-review-2021
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
partial observation
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
Multi-Agent Reinforcement Learning
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
mean field theory
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
reinforcement-learning
Bellinger, C. et al., 2021. Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning. In Canadian Conference on Artificial Intelligence. Springer, p. 12.
year-in-review-2021
Bellinger, C. et al., 2021. Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning. In Canadian Conference on Artificial Intelligence. Springer, p. 12.
Allada, A.Krishna et al., 2021. Analysis of Language Embeddings for Classification of Unstructured Pathology Reports. In International Conference of the IEEE Engineering in Medicine and Biology Society. November. IEEE, p. 4.
natural language processing
Allada, A.Krishna et al., 2021. Analysis of Language Embeddings for Classification of Unstructured Pathology Reports. In International Conference of the IEEE Engineering in Medicine and Biology Society. November. IEEE, p. 4.
digital pathology
Allada, A.Krishna et al., 2021. Analysis of Language Embeddings for Classification of Unstructured Pathology Reports. In International Conference of the IEEE Engineering in Medicine and Biology Society. November. IEEE, p. 4.
proj-digipath
Allada, A.Krishna et al., 2021. Analysis of Language Embeddings for Classification of Unstructured Pathology Reports. In International Conference of the IEEE Engineering in Medicine and Biology Society. November. IEEE, p. 4.
Deep Neural Networks
Allada, A.Krishna et al., 2021. Analysis of Language Embeddings for Classification of Unstructured Pathology Reports. In International Conference of the IEEE Engineering in Medicine and Biology Society. November. IEEE, p. 4.
digital-pathology
Sikaroudi, M. et al., 2021. Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem. In 25th International Conference on Pattern Recognition (ICPR). January. Milan, Italy (virtual): IEEE, p. 7. Available at: https://ieeexplore.ieee.org/document/9412478.
year-in-review-2021
Sikaroudi, M. et al., 2021. Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem. In 25th International Conference on Pattern Recognition (ICPR). January. Milan, Italy (virtual): IEEE, p. 7. Available at: https://ieeexplore.ieee.org/document/9412478.
manifold-learning
Sikaroudi, M. et al., 2021. Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem. In 25th International Conference on Pattern Recognition (ICPR). January. Milan, Italy (virtual): IEEE, p. 7. Available at: https://ieeexplore.ieee.org/document/9412478.

Pages