Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Zulfikar, Wazeer"'
Autor:
Zulfikar, Wazeer Deen
Episodic memory, the memory of personal experiences, is a core component of human cognition. It functions within the neural substrate to store progress towards personal goals. Thus, it influences human behavior by enriching social interactions, formi
Autor:
Chan, Samantha, Pataranutaporn, Pat, Suri, Aditya, Zulfikar, Wazeer, Maes, Pattie, Loftus, Elizabeth F.
This study examines the impact of AI on human false memories -- recollections of events that did not occur or deviate from actual occurrences. It explores false memory induction through suggestive questioning in Human-AI interactions, simulating crim
Externí odkaz:
http://arxiv.org/abs/2408.04681
Autor:
Zulfikar, Wazeer, Protyasha, Nishat, Canales, Camila, Patel, Heli, Williamson, James, Sarnie, Laura, Nowinski, Lisa, Kosmyna, Nataliya, Townsend, Paige, Yuditskaya, Sophia, Talkar, Tanya, Sarawgi, Utkarsh Oggy, McDougle, Christopher, Quatieri, Thomas, Maes, Pattie, Mody, Maria
Adults who are minimally verbal with autism spectrum disorder (mvASD) have pronounced speech difficulties linked to impaired motor skills. Existing research and clinical assessments primarily use indirect methods such as standardized tests, video-bas
Externí odkaz:
http://arxiv.org/abs/2407.08877
Autor:
Shen, Jocelyn, Kim, Yubin, Hulse, Mohit, Zulfikar, Wazeer, Alghowinem, Sharifa, Breazeal, Cynthia, Park, Hae Won
Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy datasets fall short in capturing the richness of empathy responses, often
Externí odkaz:
http://arxiv.org/abs/2405.15708
Memoro: Using Large Language Models to Realize a Concise Interface for Real-Time Memory Augmentation
Publikováno v:
Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA
People have to remember an ever-expanding volume of information. Wearables that use information capture and retrieval for memory augmentation can help but can be disruptive and cumbersome in real-world tasks, such as in social settings. To address th
Externí odkaz:
http://arxiv.org/abs/2403.02135
Reliability of machine learning (ML) systems is crucial in safety-critical applications such as healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of ML systems in deployment. Sequential and parallel ense
Externí odkaz:
http://arxiv.org/abs/2104.10715
The problem of missing data has been persistent for a long time and poses a major obstacle in machine learning and statistical data analysis. Past works in this field have tried using various data imputation techniques to fill in the missing data, or
Externí odkaz:
http://arxiv.org/abs/2011.09596
Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment. In this work, we propose an uncertainty-aware
Externí odkaz:
http://arxiv.org/abs/2010.01440
Understanding and quantifying uncertainty in black box Neural Networks (NNs) is critical when deployed in real-world settings such as healthcare. Recent works using Bayesian and non-Bayesian methods have shown how a unified predictive uncertainty can
Externí odkaz:
http://arxiv.org/abs/2009.12406
Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's
Externí odkaz:
http://arxiv.org/abs/2009.00700