Zobrazeno 1 - 10
of 33 120
pro vyhledávání: '"Lama, A"'
Autor:
Pu, Isabella, Nguyen, Golda, Alsultan, Lama, Picard, Rosalind, Breazeal, Cynthia, Alghowinem, Sharifa
Social-emotional learning (SEL) skills are essential for children to develop to provide a foundation for future relational and academic success. Using art as a medium for creation or as a topic to provoke conversation is a well-known method of SEL le
Externí odkaz:
http://arxiv.org/abs/2409.10710
Autor:
Kumar, Shachi H, Sahay, Saurav, Mazumder, Sahisnu, Okur, Eda, Manuvinakurike, Ramesh, Beckage, Nicole, Su, Hsuan, Lee, Hung-yi, Nachman, Lama
Large Language Models (LLMs) have excelled at language understanding and generating human-level text. However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the m
Externí odkaz:
http://arxiv.org/abs/2408.03907
Autor:
Zhang, Alice Qian, Shaw, Ryland, Anthis, Jacy Reese, Milton, Ashlee, Tseng, Emily, Suh, Jina, Ahmad, Lama, Kumar, Ram Shankar Siva, Posada, Julian, Shestakofsky, Benjamin, Roberts, Sarah T., Gray, Mary L.
Rapid progress in general-purpose AI has sparked significant interest in "red teaming," a practice of adversarial testing originating in military and cybersecurity applications. AI red teaming raises many questions about the human factor, such as how
Externí odkaz:
http://arxiv.org/abs/2407.07786
The importance of addressing fairness and bias in artificial intelligence (AI) systems cannot be over-emphasized. Mainstream media has been awashed with news of incidents around stereotypes and other types of bias in many of these systems in recent y
Externí odkaz:
http://arxiv.org/abs/2406.19097
Biomechanical biofeedback may enhance rehabilitation and provide clinicians with more objective task evaluation. These feedbacks often rely on expensive motion capture systems, which restricts their widespread use, leading to the development of compu
Externí odkaz:
http://arxiv.org/abs/2406.10007
Model evaluations are central to understanding the safety, risks, and societal impacts of AI systems. While most real-world AI applications involve human-AI interaction, most current evaluations (e.g., common benchmarks) of AI models do not. Instead,
Externí odkaz:
http://arxiv.org/abs/2405.10632
Autor:
Alshammari, Suad, Basalelah, Lama, Rukbah, Walaa Abu, Alsuhibani, Ali, Wijesinghe, Dayanjan S.
The exponential growth of scientific literature has resulted in information overload, challenging researchers to effectively synthesize relevant publications. This paper explores the integration of traditional reference management software with advan
Externí odkaz:
http://arxiv.org/abs/2405.07963
Thermal imaging plays a crucial role in various applications, but the inherent low resolution of commonly available infrared (IR) cameras limits its effectiveness. Conventional super-resolution (SR) methods often struggle with thermal images due to t
Externí odkaz:
http://arxiv.org/abs/2404.14533
Image classification is a fundamental task in computer vision, and the quest to enhance DNN accuracy without inflating model size or latency remains a pressing concern. We make a couple of advances in this regard, leading to a novel EncodeNet design
Externí odkaz:
http://arxiv.org/abs/2404.13770
Autor:
Vidgen, Bertie, Agrawal, Adarsh, Ahmed, Ahmed M., Akinwande, Victor, Al-Nuaimi, Namir, Alfaraj, Najla, Alhajjar, Elie, Aroyo, Lora, Bavalatti, Trupti, Bartolo, Max, Blili-Hamelin, Borhane, Bollacker, Kurt, Bomassani, Rishi, Boston, Marisa Ferrara, Campos, Siméon, Chakra, Kal, Chen, Canyu, Coleman, Cody, Coudert, Zacharie Delpierre, Derczynski, Leon, Dutta, Debojyoti, Eisenberg, Ian, Ezick, James, Frase, Heather, Fuller, Brian, Gandikota, Ram, Gangavarapu, Agasthya, Gangavarapu, Ananya, Gealy, James, Ghosh, Rajat, Goel, James, Gohar, Usman, Goswami, Sujata, Hale, Scott A., Hutiri, Wiebke, Imperial, Joseph Marvin, Jandial, Surgan, Judd, Nick, Juefei-Xu, Felix, Khomh, Foutse, Kailkhura, Bhavya, Kirk, Hannah Rose, Klyman, Kevin, Knotz, Chris, Kuchnik, Michael, Kumar, Shachi H., Kumar, Srijan, Lengerich, Chris, Li, Bo, Liao, Zeyi, Long, Eileen Peters, Lu, Victor, Luger, Sarah, Mai, Yifan, Mammen, Priyanka Mary, Manyeki, Kelvin, McGregor, Sean, Mehta, Virendra, Mohammed, Shafee, Moss, Emanuel, Nachman, Lama, Naganna, Dinesh Jinenhally, Nikanjam, Amin, Nushi, Besmira, Oala, Luis, Orr, Iftach, Parrish, Alicia, Patlak, Cigdem, Pietri, William, Poursabzi-Sangdeh, Forough, Presani, Eleonora, Puletti, Fabrizio, Röttger, Paul, Sahay, Saurav, Santos, Tim, Scherrer, Nino, Sebag, Alice Schoenauer, Schramowski, Patrick, Shahbazi, Abolfazl, Sharma, Vin, Shen, Xudong, Sistla, Vamsi, Tang, Leonard, Testuggine, Davide, Thangarasa, Vithursan, Watkins, Elizabeth Anne, Weiss, Rebecca, Welty, Chris, Wilbers, Tyler, Williams, Adina, Wu, Carole-Jean, Yadav, Poonam, Yang, Xianjun, Zeng, Yi, Zhang, Wenhui, Zhdanov, Fedor, Zhu, Jiacheng, Liang, Percy, Mattson, Peter, Vanschoren, Joaquin
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introdu
Externí odkaz:
http://arxiv.org/abs/2404.12241