Zobrazeno 1 - 10
of 12 440
pro vyhledávání: '"P. P. Kaul"'
We present a novel Hand-pose Embedding Interactive System (HpEIS) as a virtual sensor, which maps users' flexible hand poses to a two-dimensional visual space using a Variational Autoencoder (VAE) trained on a variety of hand poses. HpEIS enables vis
Externí odkaz:
http://arxiv.org/abs/2410.08779
Autor:
Gowda, Harshavardhana T., Kaul, Neha, Carrasco, Carlos, Battraw, Marcus A., Amer, Safa, Kotwal, Saniya, Lam, Selena, McNaughton, Zachary, Rahimi, Ferdous, Shehabi, Sana, Schofield, Jonathon S., Miller, Lee M.
Brain-body-computer interfaces aim to provide a fluid and natural way for humans to interact with technology. Among noninvasive interfaces, surface electromyogram (sEMG) signals have shown particular utility. However, much remains unknown about how s
Externí odkaz:
http://arxiv.org/abs/2409.19939
In extremal combinatorics, it is common to focus on structures that are minimal with respect to a certain property. In particular, critical and list-critical graphs occupy a prominent place in graph coloring theory. Stiebitz, Tuza, and Voigt introduc
Externí odkaz:
http://arxiv.org/abs/2408.04538
Publikováno v:
Phys. Rev. B 110, 094416 (2024)
We study the transition between N\'eel and columnar valence-bond solid ordering in two-dimensional $S=3/2$ square lattice quantum antiferromagnets with SO(3) symmetry. According to the deconfined criticality scenario, this transition can be direct an
Externí odkaz:
http://arxiv.org/abs/2407.07334
Autor:
Devaguptapu, Chaitanya, Aithal, Sumukh, Ramasubramanian, Shrinivas, Yamada, Moyuru, Kaul, Manohar
Self-supervised learning (SSL) with vision transformers (ViTs) has proven effective for representation learning as demonstrated by the impressive performance on various downstream tasks. Despite these successes, existing ViT-based SSL architectures d
Externí odkaz:
http://arxiv.org/abs/2406.12944
Autor:
Tragakis, Athanasios, Aversa, Marco, Kaul, Chaitanya, Murray-Smith, Roderick, Faccio, Daniele
In this work, we introduce Pixelsmith, a zero-shot text-to-image generative framework to sample images at higher resolutions with a single GPU. We are the first to show that it is possible to scale the output of a pre-trained diffusion model by a fac
Externí odkaz:
http://arxiv.org/abs/2406.07251
Given unstructured text, Large Language Models (LLMs) are adept at answering simple (single-hop) questions. However, as the complexity of the questions increase, the performance of LLMs degrade. We believe this is due to the overhead associated with
Externí odkaz:
http://arxiv.org/abs/2406.06027
We introduce a simple lattice spin model that is written in terms of the well-known four-dimensional $\gamma$-matrix representation of the Clifford algebra. The local spins with a four-dimensional Hilbert space transform in a spinorial $(1/2,0) \oplu
Externí odkaz:
http://arxiv.org/abs/2406.04120
Autor:
Korablyov, Maksym, Liu, Cheng-Hao, Jain, Moksh, van der Sloot, Almer M., Jolicoeur, Eric, Ruediger, Edward, Nica, Andrei Cristian, Bengio, Emmanuel, Lapchevskyi, Kostiantyn, St-Cyr, Daniel, Schuetz, Doris Alexandra, Butoi, Victor Ion, Rector-Brooks, Jarrid, Blackburn, Simon, Feng, Leo, Nekoei, Hadi, Gottipati, SaiKrishna, Vijayan, Priyesh, Gupta, Prateek, Rampášek, Ladislav, Avancha, Sasikanth, Bacon, Pierre-Luc, Hamilton, William L., Paige, Brooks, Misra, Sanchit, Jastrzebski, Stanislaw Kamil, Kaul, Bharat, Precup, Doina, Hernández-Lobato, José Miguel, Segler, Marwin, Bronstein, Michael, Marinier, Anne, Tyers, Mike, Bengio, Yoshua
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active lear
Externí odkaz:
http://arxiv.org/abs/2405.01616
Autor:
Agrawal, Vikhyat, Kalmady, Sunil Vasu, Malipeddi, Venkataseetharam Manoj, Manthena, Manisimha Varma, Sun, Weijie, Islam, Saiful, Hindle, Abram, Kaul, Padma, Greiner, Russell
This research paper explores ways to apply Federated Learning (FL) and Differential Privacy (DP) techniques to population-scale Electrocardiogram (ECG) data. The study learns a multi-label ECG classification model using FL and DP based on 1,565,849 E
Externí odkaz:
http://arxiv.org/abs/2405.00725