Zobrazeno 1 - 10
of 683
pro vyhledávání: '"A Dognin"'
Autor:
Miehling, Erik, Desmond, Michael, Ramamurthy, Karthikeyan Natesan, Daly, Elizabeth M., Dognin, Pierre, Rios, Jesus, Bouneffouf, Djallel, Liu, Miao
Building pluralistic AI requires designing models that are able to be shaped to represent a wide range of value systems and cultures. Achieving this requires first being able to evaluate the degree to which a given model is capable of reflecting vari
Externí odkaz:
http://arxiv.org/abs/2411.12405
Low-dimensional embeddings are a cornerstone in the modelling and analysis of complex networks. However, most existing approaches for mining network embedding spaces rely on computationally intensive machine learning systems to facilitate downstream
Externí odkaz:
http://arxiv.org/abs/2410.01865
Autor:
Lee, Bruce W., Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Miehling, Erik, Dognin, Pierre, Nagireddy, Manish, Dhurandhar, Amit
LLMs have shown remarkable capabilities, but precisely controlling their response behavior remains challenging. Existing activation steering methods alter LLM behavior indiscriminately, limiting their practical applicability in settings where selecti
Externí odkaz:
http://arxiv.org/abs/2409.05907
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
Random walks are widely used for mining networks due to the computational efficiency of computing them. For instance, graph representation learning learns a d-dimensional embedding space, so that the nodes that tend to co-occur on random walks (a pro
Externí odkaz:
http://arxiv.org/abs/2405.14194
Autor:
Przulj, Natasa, Malod-Dognin, Noel
Heterogeneous, interconnected, systems-level, molecular data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known
Externí odkaz:
http://arxiv.org/abs/2405.09595
Autor:
Dognin, Pierre, Rios, Jesus, Luss, Ronny, Padhi, Inkit, Riemer, Matthew D, Liu, Miao, Sattigeri, Prasanna, Nagireddy, Manish, Varshney, Kush R., Bouneffouf, Djallel
Developing value-aligned AI agents is a complex undertaking and an ongoing challenge in the field of AI. Specifically within the domain of Large Language Models (LLMs), the capability to consolidate multiple independently trained dialogue agents, eac
Externí odkaz:
http://arxiv.org/abs/2403.12805
Autor:
Achintalwar, Swapnaja, Garcia, Adriana Alvarado, Anaby-Tavor, Ateret, Baldini, Ioana, Berger, Sara E., Bhattacharjee, Bishwaranjan, Bouneffouf, Djallel, Chaudhury, Subhajit, Chen, Pin-Yu, Chiazor, Lamogha, Daly, Elizabeth M., DB, Kirushikesh, de Paula, Rogério Abreu, Dognin, Pierre, Farchi, Eitan, Ghosh, Soumya, Hind, Michael, Horesh, Raya, Kour, George, Lee, Ja Young, Madaan, Nishtha, Mehta, Sameep, Miehling, Erik, Murugesan, Keerthiram, Nagireddy, Manish, Padhi, Inkit, Piorkowski, David, Rawat, Ambrish, Raz, Orna, Sattigeri, Prasanna, Strobelt, Hendrik, Swaminathan, Sarathkrishna, Tillmann, Christoph, Trivedi, Aashka, Varshney, Kush R., Wei, Dennis, Witherspooon, Shalisha, Zalmanovici, Marcel
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (training cost, API access, data availability, etc.), it may not always be
Externí odkaz:
http://arxiv.org/abs/2403.06009
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:
Maier, Andreas, Hartung, Michael, Abovsky, Mark, Adamowicz, Klaudia, Bader, Gary D., Baier, Sylvie, Blumenthal, David B., Chen, Jing, Elkjaer, Maria L., Garcia-Hernandez, Carlos, Helmy, Mohamed, Hoffmann, Markus, Jurisica, Igor, Kotlyar, Max, Lazareva, Olga, Levi, Hagai, List, Markus, Lobentanzer, Sebastian, Loscalzo, Joseph, Malod-Dognin, Noel, Manz, Quirin, Matschinske, Julian, Mee, Miles, Oubounyt, Mhaned, Pico, Alexander R., Pillich, Rudolf T., Poschenrieder, Julian M., Pratt, Dexter, Pržulj, Nataša, Sadegh, Sepideh, Saez-Rodriguez, Julio, Sarkar, Suryadipto, Shaked, Gideon, Shamir, Ron, Trummer, Nico, Turhan, Ugur, Wang, Ruisheng, Zolotareva, Olga, Baumbach, Jan
In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerg
Externí odkaz:
http://arxiv.org/abs/2305.15453