Zobrazeno 1 - 10
of 316
pro vyhledávání: '"NATARAJAN, SRIRAAM"'
Human-in-the-loop (HIL) systems have emerged as a promising approach for combining the strengths of data-driven machine learning models with the contextual understanding of human experts. However, a deeper look into several of these systems reveals t
Externí odkaz:
http://arxiv.org/abs/2412.14232
Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. Nevertheless, the existing body of research on PCs predominantly concentrates on data-driven parameter learning, ofte
Externí odkaz:
http://arxiv.org/abs/2405.02413
Autor:
Sidheekh, Sahil, Tenali, Pranuthi, Mathur, Saurabh, Blasch, Erik, Kersting, Kristian, Natarajan, Sriraam
We consider the problem of late multi-modal fusion for discriminative learning. Motivated by noisy, multi-source domains that require understanding the reliability of each data source, we explore the notion of credibility in the context of multi-moda
Externí odkaz:
http://arxiv.org/abs/2403.03281
Autor:
Sidheekh, Sahil, Natarajan, Sriraam
We present a comprehensive survey of the advancements and techniques in the field of tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits (PCs). We provide a unified perspective on the inherent trade-offs between
Externí odkaz:
http://arxiv.org/abs/2402.00759
Building teams and promoting collaboration are two very common business activities. An example of these are seen in the TeamingForFunding problem, where research institutions and researchers are interested to identify collaborative opportunities when
Externí odkaz:
http://arxiv.org/abs/2309.09404
We consider the problem of identifying authorship by posing it as a knowledge graph construction and refinement. To this effect, we model this problem as learning a probabilistic logic model in the presence of human guidance (knowledge-based learning
Externí odkaz:
http://arxiv.org/abs/2309.05681
These days automation is being applied everywhere. In every environment, planning for the actions to be taken by the agents is an important aspect. In this paper, we plan to implement planning for multi-agents with a centralized controller. We compar
Externí odkaz:
http://arxiv.org/abs/2302.03800
Autor:
Kokel, Harsha, Das, Mayukh, Islam, Rakibul, Bonn, Julia, Cai, Jon, Dan, Soham, Narayan-Chen, Anjali, Jayannavar, Prashant, Doppa, Janardhan Rao, Hockenmaier, Julia, Natarajan, Sriraam, Palmer, Martha, Roth, Dan
We consider the problem of human-machine collaborative problem solving as a planning task coupled with natural language communication. Our framework consists of three components -- a natural language engine that parses the language utterances to a fo
Externí odkaz:
http://arxiv.org/abs/2207.09566
Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of
Externí odkaz:
http://arxiv.org/abs/2206.07904
Autor:
Karanam, Athresh, Mathur, Saurabh, Radivojac, Predrag, Haas, David M., Kersting, Kristian, Natarajan, Sriraam
Publikováno v:
PMLR 186:325-336 (2022)
We consider the problem of explaining a class of tractable deep probabilistic models, the Sum-Product Networks (SPNs) and present an algorithm ExSPN to generate explanations. To this effect, we define the notion of a context-specific independence tre
Externí odkaz:
http://arxiv.org/abs/2110.09778