Zobrazeno 1 - 10
of 30 411
pro vyhledávání: '"P, Erickson"'
In atonal music theory, given a microtonal scale consisting of $n$ pitches, two chords are said to be Z-related if they have the same multiset of intervals between pitches. (This is mathematically equivalent to the study of homometric subsets of $\ma
Externí odkaz:
http://arxiv.org/abs/2412.08997
Supervised machine learning methods require large-scale training datasets to perform well in practice. Synthetic data has been showing great progress recently and has been used as a complement to real data. However, there is yet a great urge to asses
Externí odkaz:
http://arxiv.org/abs/2412.05466
Autor:
Erickson, Brittany A.
In this work we explore the fidelity of numerical approximations to continuous spectra of hyperbolic partial differential equation systems. We are particularly interested in the ability of discrete methods to accurately discover sources of physical i
Externí odkaz:
http://arxiv.org/abs/2412.05399
Autor:
Slavkova, Kalina P., Traughber, Melanie, Chen, Oliver, Bakos, Robert, Goldstein, Shayna, Harms, Dan, Erickson, Bradley J., Siddiqui, Khan M.
Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential o
Externí odkaz:
http://arxiv.org/abs/2411.17891
Autor:
Pohjonen, Joona, Batouche, Abderrahim-Oussama, Rannikko, Antti, Sandeman, Kevin, Erickson, Andrew, Pitkanen, Esa, Mirtti, Tuomas
Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources. Here, we develop a foundation model for prostate cancer digital path
Externí odkaz:
http://arxiv.org/abs/2411.11458
Offline reinforcement learning can enable policy learning from pre-collected, sub-optimal datasets without online interactions. This makes it ideal for real-world robots and safety-critical scenarios, where collecting online data or expert demonstrat
Externí odkaz:
http://arxiv.org/abs/2411.05273
Autor:
Meilander, Jeff, Herman, Chloe, Manley, Andrew, Augustine, Georgia, Birdsell, Dawn, Bolyen, Evan, Celona, Kimberly R., Coffey, Hayden, Cocking, Jill, Donoghue, Teddy, Draves, Alexis, Erickson, Daryn, Foley, Marissa, Gehret, Liz, Hagen, Johannah, Hepp, Crystal, Ingram, Parker, John, David, Kadar, Katarina, Keim, Paul, Lloyd, Victoria, Osterink, Christina, Queeney, Victoria, Ramirez, Diego, Romero, Antonio, Ruby, Megan C., Sahl, Jason W., Soloway, Sydni, Stone, Nathan E., Trottier, Shannon, Van Orden, Kaleb, Painter, Alexis, Wallace, Sam, Wilcox, Larissa, Wood, Colin V., Yancey, Jaiden, Caporaso, J. Gregory
Human excrement composting (HEC) is a sustainable strategy for human excrement (HE) management that recycles nutrients and mitigates health risks while reducing reliance on freshwater, fossil fuels, and fertilizers. We present a comprehensive microbi
Externí odkaz:
http://arxiv.org/abs/2411.04148
This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms. The ability of an EMG classifier to handle inputs drawn
Externí odkaz:
http://arxiv.org/abs/2410.23625
Autor:
Yuan, Jessie, Gupta, Janavi, Padmanabha, Akhil, Karachiwalla, Zulekha, Majidi, Carmel, Admoni, Henny, Erickson, Zackory
Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who are unable to complete activities of daily living (ADLs). Speech inter
Externí odkaz:
http://arxiv.org/abs/2410.20624
Autor:
Li, Tian, Collett, Thomas E., Marshall, Philip J., Erickson, Sydney, Enzi, Wolfgang, Oldham, Lindsay, Ballard, Daniel
The time delay between multiple images of strongly lensed quasars has been used to infer the Hubble constant. The primary systematic uncertainty for time-delay cosmography is the mass-sheet transform (MST), which preserves the lensing observables whi
Externí odkaz:
http://arxiv.org/abs/2410.16171