Zobrazeno 1 - 10
of 418 059
pro vyhledávání: '"Reddy AS"'
Autor:
Böhlen, Marc, Sughiarta, Gede, Kurnianingsih, Atiek, Gopaladinne, Srikar Reddy, Shrivastava, Sujay, Gorla, Hemanth Kumar Reddy
This paper describes spatially aware Artificial Intelligence, GeoAI, tailored for small organizations such as NGOs in resource constrained contexts where access to large datasets, expensive compute infrastructure and AI expertise may be restricted. W
Externí odkaz:
http://arxiv.org/abs/2408.17361
Autor:
Gudepu, Venkateswarlu, Chirumamilla, Bhargav, Chintapalli, Venkatarami Reddy, Castoldi, Piero, Valcarenghi, Luca, Tamma, Bheemarjuna Reddy, Kondepu, Koteswararao
Beyond fifth-generation (B5G) networks aim to support high data rates, low-latency applications, and massive machine communications. Artificial Intelligence/Machine Learning (AI/ML) can help to improve B5G network performance and efficiency. However,
Externí odkaz:
http://arxiv.org/abs/2408.14827
Large language models (LLMs) often show unwarranted preference for certain choice options when responding to multiple-choice questions, posing significant reliability concerns in LLM-automated systems. To mitigate this selection bias problem, previou
Externí odkaz:
http://arxiv.org/abs/2409.18857
Autor:
Sidhu, Mankeerat, Chopra, Hetarth, Blume, Ansel, Kim, Jeonghwan, Reddy, Revanth Gangi, Ji, Heng
In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retrieves a set of positive and negative images of an object to ground, em
Externí odkaz:
http://arxiv.org/abs/2409.18733
Adolescents with chronic illnesses need to learn self-management skills in preparation for the transition from pediatric to adult healthcare, which is associated with negative health outcomes for youth. However, few studies have explored how adolesce
Externí odkaz:
http://arxiv.org/abs/2409.18275
Autor:
Dementyev, Artem, Reddy, Chandan K. A., Wisdom, Scott, Chatlani, Navin, Hershey, John R., Lyon, Richard F.
Low latency models are critical for real-time speech enhancement applications, such as hearing aids and hearables. However, the sub-millisecond latency space for resource-constrained hearables remains underexplored. We demonstrate speech enhancement
Externí odkaz:
http://arxiv.org/abs/2409.18239
Autor:
Lorenz, Brian, Kriek, Mariska, Shapley, Alice E., Sanders, Ryan L., Coil, Alison L., Leja, Joel, Mobasher, Bahram, Nelson, Erica, Price, Sedona H., Reddy, Naveen A., Runco, Jordan N., Suess, Katherine A., Shivaei, Irene, Siana, Brian, Weisz, Daniel R.
We examine star-formation and dust properties for a sample of 660 galaxies at $1.37\leq z\leq 2.61$ in the MOSDEF survey by dividing them into groups with similarly-shaped spectral energy distributions (SEDs). For each group, we combine the galaxy ph
Externí odkaz:
http://arxiv.org/abs/2409.18179
Detecting and measuring confounding effects from data is a key challenge in causal inference. Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables. Causal sufficiency is both unrealistic
Externí odkaz:
http://arxiv.org/abs/2409.17840
Autor:
Covelo-Paz, Alba, Giovinazzo, Emma, Oesch, Pascal A., Meyer, Romain A., Weibel, Andrea, Brammer, Gabriel, Fudamoto, Yoshinobu, Kerutt, Josephine, Lin, Jamie, Matharu, Jasleen, Naidu, Rohan P., Velichko, Anna, Bollo, Victoria, Bouwens, Rychard, Chisholm, John, Illingworth, Garth D., Kramarenko, Ivan, Magee, Daniel, Maseda, Michael, Matthee, Jorryt, Nelson, Erica, Reddy, Naveen, Schaerer, Daniel, Stefanon, Mauro, Xiao, Mengyuan
The H{\alpha} nebular emission line is an optimal tracer for recent star formation in galaxies. With the advent of JWST, this line has recently become observable at z>3 for the first time. We present a catalog of 1013 H{\alpha} emitters at 3.7
Externí odkaz:
http://arxiv.org/abs/2409.17241
Autor:
Ashvin, Aditya, Lahiri, Rimita, Kommineni, Aditya, Bishop, Somer, Lord, Catherine, Kadiri, Sudarsana Reddy, Narayanan, Shrikanth
The ability to reliably transcribe child-adult conversations in a clinical setting is valuable for diagnosis and understanding of numerous developmental disorders such as Autism Spectrum Disorder. Recent advances in deep learning architectures and av
Externí odkaz:
http://arxiv.org/abs/2409.16135