Zobrazeno 1 - 10
of 24 164
pro vyhledávání: '"McDermott P"'
Autor:
Mărgărit, Horia, Bowman, Amanda, Karuppasamy, Krishnageetha, Maldonado-Romo, Alberto, Sahgal, Vardaan, McDermott, Brian J.
In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges en
Externí odkaz:
http://arxiv.org/abs/2411.12920
Autor:
Mohseni, Masoud, Scherer, Artur, Johnson, K. Grace, Wertheim, Oded, Otten, Matthew, Aadit, Navid Anjum, Bresniker, Kirk M., Camsari, Kerem Y., Chapman, Barbara, Chatterjee, Soumitra, Dagnew, Gebremedhin A., Esposito, Aniello, Fahim, Farah, Fiorentino, Marco, Khalid, Abdullah, Kong, Xiangzhou, Kulchytskyy, Bohdan, Li, Ruoyu, Lott, P. Aaron, Markov, Igor L., McDermott, Robert F., Pedretti, Giacomo, Gajjar, Archit, Silva, Allyson, Sorebo, John, Spentzouris, Panagiotis, Steiner, Ziv, Torosov, Boyan, Venturelli, Davide, Visser, Robert J., Webb, Zak, Zhan, Xin, Cohen, Yonatan, Ronagh, Pooya, Ho, Alan, Beausoleil, Raymond G., Martinis, John M.
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubi
Externí odkaz:
http://arxiv.org/abs/2411.10406
Autor:
McDermott, Matthew, Rife, Jason
In this paper we reexamine the process through which a Neural Radiance Field (NeRF) can be trained to produce novel LiDAR views of a scene. Unlike image applications where camera pixels integrate light over time, LiDAR pulses arrive at specific times
Externí odkaz:
http://arxiv.org/abs/2411.01725
Autor:
Oufattole, Nassim, Bergamaschi, Teya, Kolo, Aleksia, Jeong, Hyewon, Gaggin, Hanna, Stultz, Collin M., McDermott, Matthew B. A.
Effective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-quality baseline models for diverse supervised learning tasks in an eff
Externí odkaz:
http://arxiv.org/abs/2411.00200
Autor:
Fortman, Margaret A., Harrison, David C., Rodriguez, Ramiro H., Krebs, Zachary J., Han, Sangjun, Jang, Min Seok, McDermott, Robert, Girit, Caglar O., Brar, Victor W.
Josephson junction spectroscopy is a powerful local microwave spectroscopy technique that has promising potential as a diagnostic tool to probe the microscopic origins of noise in superconducting qubits. We present advancements toward realizing Josep
Externí odkaz:
http://arxiv.org/abs/2410.03009
Autor:
Steinberg, Ethan, Wornow, Michael, Bedi, Suhana, Fries, Jason Alan, McDermott, Matthew B. A., Shah, Nigam H.
The growing demand for machine learning in healthcare requires processing increasingly large electronic health record (EHR) datasets, but existing pipelines are not computationally efficient or scalable. In this paper, we introduce meds_reader, an op
Externí odkaz:
http://arxiv.org/abs/2409.09095
This paper introduces a novel training framework called Focused Discriminative Training (FDT) to further improve streaming word-piece end-to-end (E2E) automatic speech recognition (ASR) models trained using either CTC or an interpolation of CTC and a
Externí odkaz:
http://arxiv.org/abs/2408.13008
Autor:
Smith, Trevor R., McDermott, Spencer, Patel, Vatsalkumar, Anthony, Ross, Hedge, Manu, Knights, Andrew P., Lewis, Ryan B.
The explosion of artificial intelligence, possible end of Moore's law, dawn of quantum computing and continued exponential growth of data communications traffic have brought new urgency to the need for laser integration on the diversified Si platform
Externí odkaz:
http://arxiv.org/abs/2408.03253
Autor:
Jajoria, Pushkar, McDermott, James
This study introduces a text-conditioned approach to generating drumbeats with Latent Diffusion Models (LDMs). It uses informative conditioning text extracted from training data filenames. By pretraining a text and drumbeat encoder through contrastiv
Externí odkaz:
http://arxiv.org/abs/2408.02711
Publikováno v:
Transactions on Machine Learning Research (TMLR), 2024
Perturbation robustness evaluates the vulnerabilities of models, arising from a variety of perturbations, such as data corruptions and adversarial attacks. Understanding the mechanisms of perturbation robustness is critical for global interpretabilit
Externí odkaz:
http://arxiv.org/abs/2408.01139