Zobrazeno 1 - 10
of 4 645
pro vyhledávání: '"P Teddy"'
Autor:
Kinakh, Vitaliy, Pulfer, Brian, Belousov, Yury, Fernandez, Pierre, Furon, Teddy, Voloshynovskiy, Slava
The vast amounts of digital content captured from the real world or AI-generated media necessitate methods for copyright protection, traceability, or data provenance verification. Digital watermarking serves as a crucial approach to address these cha
Externí odkaz:
http://arxiv.org/abs/2409.18211
Scholarly communication is vital to scientific advancement, enabling the exchange of ideas and knowledge. When selecting publication venues, scholars consider various factors, such as journal relevance, reputation, outreach, and editorial standards a
Externí odkaz:
http://arxiv.org/abs/2409.12158
Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires are becoming increasingly frequent. Although they occur less frequentl
Externí odkaz:
http://arxiv.org/abs/2409.10046
Autor:
Summerfield, Christopher, Argyle, Lisa, Bakker, Michiel, Collins, Teddy, Durmus, Esin, Eloundou, Tyna, Gabriel, Iason, Ganguli, Deep, Hackenburg, Kobi, Hadfield, Gillian, Hewitt, Luke, Huang, Saffron, Landemore, Helene, Marchal, Nahema, Ovadya, Aviv, Procaccia, Ariel, Risse, Mathias, Schneier, Bruce, Seger, Elizabeth, Siddarth, Divya, Sætra, Henrik Skaug, Tessler, MH, Botvinick, Matthew
Advanced AI systems capable of generating humanlike text and multimodal content are now widely available. In this paper, we discuss the impacts that generative artificial intelligence may have on democratic processes. We consider the consequences of
Externí odkaz:
http://arxiv.org/abs/2409.06729
The analysis of tabular datasets is highly prevalent both in scientific research and real-world applications of Machine Learning (ML). Unlike many other ML tasks, Deep Learning (DL) models often do not outperform traditional methods in this area. Pre
Externí odkaz:
http://arxiv.org/abs/2408.14817
Autor:
Zhang, Andy K., Perry, Neil, Dulepet, Riya, Ji, Joey, Lin, Justin W., Jones, Eliot, Menders, Celeste, Hussein, Gashon, Liu, Samantha, Jasper, Donovan, Peetathawatchai, Pura, Glenn, Ari, Sivashankar, Vikram, Zamoshchin, Daniel, Glikbarg, Leo, Askaryar, Derek, Yang, Mike, Zhang, Teddy, Alluri, Rishi, Tran, Nathan, Sangpisit, Rinnara, Yiorkadjis, Polycarpos, Osele, Kenny, Raghupathi, Gautham, Boneh, Dan, Ho, Daniel E., Liang, Percy
Language Model (LM) agents for cybersecurity that are capable of autonomously identifying vulnerabilities and executing exploits have the potential to cause real-world impact. Policymakers, model providers, and other researchers in the AI and cyberse
Externí odkaz:
http://arxiv.org/abs/2408.08926
We investigate the lazy burning process for Latin squares by studying their associated hypergraphs. In lazy burning, a set of vertices in a hypergraph is initially burned, and that burning spreads to neighboring vertices over time via a specified pro
Externí odkaz:
http://arxiv.org/abs/2407.20370
This paper proposes a novel approach towards image authentication and tampering detection by using watermarking as a communication channel for semantic information. We modify the HiDDeN deep-learning watermarking architecture to embed and extract hig
Externí odkaz:
http://arxiv.org/abs/2407.18995
We use majorization to model comparative diversity in school choice. A population of agents is more diverse than another population of agents if its distribution over groups is less concentrated: being less concentrated takes a specific mathematical
Externí odkaz:
http://arxiv.org/abs/2407.17589
Autor:
Lazebnik, Teddy
Detecting and understanding out-of-distribution (OOD) samples is crucial in machine learning (ML) to ensure reliable model performance. Current OOD studies, in general, and in the context of ML, in particular, primarily focus on extrapolatory OOD (ou
Externí odkaz:
http://arxiv.org/abs/2407.04534