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pro vyhledávání: '"Yalavarthi A"'
In mission-critical domains such as law enforcement and medical diagnosis, the ability to explain and interpret the outputs of deep learning models is crucial for ensuring user trust and supporting informed decision-making. Despite advancements in ex
Externí odkaz:
http://arxiv.org/abs/2411.04008
Autor:
Yalavarthi, Vijaya Krishna, Scholz, Randolf, Madhusudhanan, Kiran, Born, Stefan, Schmidt-Thieme, Lars
Probabilistic forecasting models for joint distributions of targets in irregular time series are a heavily under-researched area in machine learning with, to the best of our knowledge, only three models researched so far: GPR, the Gaussian Process Re
Externí odkaz:
http://arxiv.org/abs/2406.07246
Autor:
Klötergens, Christian, Yalavarthi, Vijaya Krishna, Stubbemann, Maximilian, Schmidt-Thieme, Lars
Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challenge to standard deep learning models that operate only on fully observ
Externí odkaz:
http://arxiv.org/abs/2405.03582
Autor:
Malik, Aditya, Ratha, Nalini, Yalavarthi, Bharat, Sharma, Tilak, Kaushik, Arjun, Jutla, Charanjit
With the rapid surge in the prevalence of Large Language Models (LLMs), individuals are increasingly turning to conversational AI for initial insights across various domains, including health-related inquiries such as disease diagnosis. Many users se
Externí odkaz:
http://arxiv.org/abs/2405.02790
Modern face recognition systems utilize deep neural networks to extract salient features from a face. These features denote embeddings in latent space and are often stored as templates in a face recognition system. These embeddings are susceptible to
Externí odkaz:
http://arxiv.org/abs/2404.16255
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
Autor:
Burchert, Johannes, Werner, Thorben, Yalavarthi, Vijaya Krishna, de Portugal, Diego Coello, Stubbemann, Maximilian, Schmidt-Thieme, Lars
As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as with any other time series data. For EEG classification many models have
Externí odkaz:
http://arxiv.org/abs/2404.06966
Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate. State-of-the-art methods for the task estimate only marginal distribu
Externí odkaz:
http://arxiv.org/abs/2402.06293
Autor:
Zhang, Chang, Yalavarthi, Eeswar K., Giroux, Mathieu, Cui, Wei, Stephan, Michel, Maleki, Ali, Weck, Arnaud, Ménard, Jean-Michel, St-Gelais, Raphael
We achieve high detectivity terahertz sensing using a silicon nitride nanomechanical resonator functionalized with a metasurface absorber. High performances are achieved by striking a fine balance between the frequency stability of the resonator, and
Externí odkaz:
http://arxiv.org/abs/2401.16503
Publikováno v:
Journal of Physics D: Applied Physics, 2024-04-24
In the recent past, significant research efforts have been put forth to fabricate low-cost noble metal-free substrates for surface-enhanced Raman spectroscopy (SERS) applications. Here we propose semiconducting TiO2 multi-leg nanotubes (TiO2 MLNTs, w
Externí odkaz:
http://arxiv.org/abs/2311.03141
Autor:
Fandio, Défi Junior Jubgang, Vishnuradhan, Aswin, Yalavarthi, Eeswar Kumar, Cui, Wei, Couture, Nicolas, Gamouras, Angela, Ménard, Jean-Michel
We combine parametric frequency upconversion with single-photon counting technology to achieve detection sensitivity down to the terahertz (THz) single-photon level. Our relatively simple detection scheme employs a near-infrared ultrafast source, a G
Externí odkaz:
http://arxiv.org/abs/2310.08452