Zobrazeno 1 - 10
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pro vyhledávání: '"Natesan P"'
Autor:
Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Sattigeri, Prasanna, Nagireddy, Manish, Dognin, Pierre, Varshney, Kush R.
Aligning large language models (LLMs) to value systems has emerged as a significant area of research within the fields of AI and NLP. Currently, this alignment process relies on the availability of high-quality supervised and preference data, which c
Externí odkaz:
http://arxiv.org/abs/2408.10392
The ability to summarize and organize knowledge into abstract concepts is key to learning and reasoning. Many industrial applications rely on the consistent and systematic use of concepts, especially when dealing with decision-critical knowledge. How
Externí odkaz:
http://arxiv.org/abs/2405.20163
Autor:
Natesan, Sanjay, Beigi, Homayoon
Publikováno v:
Recognition Technologies, Inc. Technical Report (2024), RTI-20240524-01
Large scale machine learning-based Raga identification continues to be a nontrivial issue in the computational aspects behind Carnatic music. Each raga consists of many unique and intrinsic melodic patterns that can be used to easily identify them fr
Externí odkaz:
http://arxiv.org/abs/2405.16000
Autor:
Paes, Lucas Monteiro, Wei, Dennis, Do, Hyo Jin, Strobelt, Hendrik, Luss, Ronny, Dhurandhar, Amit, Nagireddy, Manish, Ramamurthy, Karthikeyan Natesan, Sattigeri, Prasanna, Geyer, Werner, Ghosh, Soumya
Perturbation-based explanation methods such as LIME and SHAP are commonly applied to text classification. This work focuses on their extension to generative language models. To address the challenges of text as output and long text inputs, we propose
Externí odkaz:
http://arxiv.org/abs/2403.14459
Autor:
Achintalwar, Swapnaja, Baldini, Ioana, Bouneffouf, Djallel, Byamugisha, Joan, Chang, Maria, Dognin, Pierre, Farchi, Eitan, Makondo, Ndivhuwo, Mojsilovic, Aleksandra, Nagireddy, Manish, Ramamurthy, Karthikeyan Natesan, Padhi, Inkit, Raz, Orna, Rios, Jesus, Sattigeri, Prasanna, Singh, Moninder, Thwala, Siphiwe, Uceda-Sosa, Rosario A., Varshney, Kush R.
The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In contrast, in this article, we present an approach and architecture that
Externí odkaz:
http://arxiv.org/abs/2403.09704
Autor:
Dhurandhar, Amit, Nair, Rahul, Singh, Moninder, Daly, Elizabeth, Ramamurthy, Karthikeyan Natesan
Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require human responses which are expensive to acquire or use pairs of LLMs to e
Externí odkaz:
http://arxiv.org/abs/2402.14860
Given the black box nature of machine learning models, a plethora of explainability methods have been developed to decipher the factors behind individual decisions. In this paper, we introduce a novel problem of black box (probabilistic) explanation
Externí odkaz:
http://arxiv.org/abs/2402.11168
Autor:
Papamarkou, Theodore, Birdal, Tolga, Bronstein, Michael, Carlsson, Gunnar, Curry, Justin, Gao, Yue, Hajij, Mustafa, Kwitt, Roland, Liò, Pietro, Di Lorenzo, Paolo, Maroulas, Vasileios, Miolane, Nina, Nasrin, Farzana, Ramamurthy, Karthikeyan Natesan, Rieck, Bastian, Scardapane, Simone, Schaub, Michael T., Veličković, Petar, Wang, Bei, Wang, Yusu, Wei, Guo-Wei, Zamzmi, Ghada
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation
Externí odkaz:
http://arxiv.org/abs/2402.08871
Autor:
Hajij, Mustafa, Papillon, Mathilde, Frantzen, Florian, Agerberg, Jens, AlJabea, Ibrahem, Ballester, Ruben, Battiloro, Claudio, Bernárdez, Guillermo, Birdal, Tolga, Brent, Aiden, Chin, Peter, Escalera, Sergio, Fiorellino, Simone, Gardaa, Odin Hoff, Gopalakrishnan, Gurusankar, Govil, Devendra, Hoppe, Josef, Karri, Maneel Reddy, Khouja, Jude, Lecha, Manuel, Livesay, Neal, Meißner, Jan, Mukherjee, Soham, Nikitin, Alexander, Papamarkou, Theodore, Prílepok, Jaro, Ramamurthy, Karthikeyan Natesan, Rosen, Paul, Guzmán-Sáenz, Aldo, Salatiello, Alessandro, Samaga, Shreyas N., Scardapane, Simone, Schaub, Michael T., Scofano, Luca, Spinelli, Indro, Telyatnikov, Lev, Truong, Quang, Walters, Robin, Yang, Maosheng, Zaghen, Olga, Zamzmi, Ghada, Zia, Ali, Miolane, Nina
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. To
Externí odkaz:
http://arxiv.org/abs/2402.02441
Due to their ability to model meaningful higher order relations among a set of entities, higher order network models have emerged recently as a powerful alternative for graph-based network models which are only capable of modeling binary relationship
Externí odkaz:
http://arxiv.org/abs/2312.11862