Zobrazeno 1 - 10
of 69 569
pro vyhledávání: '"A Karthik"'
Autor:
Xu, Maxwell A., Narain, Jaya, Darnell, Gregory, Hallgrimsson, Haraldur, Jeong, Hyewon, Forde, Darren, Fineman, Richard, Raghuram, Karthik J., Rehg, James M., Ren, Shirley
We present RelCon, a novel self-supervised \textit{Rel}ative \textit{Con}trastive learning approach that uses a learnable distance measure in combination with a softened contrastive loss for training an motion foundation model from wearable sensors.
Externí odkaz:
http://arxiv.org/abs/2411.18822
Autor:
Bhola, Sahil, Duraisamy, Karthik
Modern computer architectures support low-precision arithmetic, which present opportunities for the adoption of mixed-precision algorithms to achieve high computational throughput and reduce energy consumption. As a growing number of scientific compu
Externí odkaz:
http://arxiv.org/abs/2411.18747
The reliable recovery and uncertainty quantification of a fixed effect function $\mu$ in a functional mixed model, for modelling population- and object-level variability in noisily observed functional data, is a notoriously challenging task: variatio
Externí odkaz:
http://arxiv.org/abs/2411.18416
This paper presents an experimental study of Kolmogorov-Arnold Networks (KANs) applied to computer vision tasks, particularly image classification. KANs introduce learnable activation functions on edges, offering flexible non-linear transformations c
Externí odkaz:
http://arxiv.org/abs/2411.18224
The remaining useful life (RUL) estimation is an important metric that helps in condition-based maintenance. Damage data obtained from the diagnostics techniques are often noisy and the RUL estimated from the data is less reliable. Estimating the pro
Externí odkaz:
http://arxiv.org/abs/2411.17824
Previous work has attempted to boost Large Language Model (LLM) performance on planning and scheduling tasks through a variety of prompt engineering techniques. While these methods can work within the distributions tested, they are neither robust nor
Externí odkaz:
http://arxiv.org/abs/2411.14484
Our research aims to probe the anisotropic matter field around black holes using black hole perturbation theory. Black holes in the universe are usually surrounded by matter or fields, and it is important to study the perturbation and the characteris
Externí odkaz:
http://arxiv.org/abs/2411.11629
Graphs inherently capture dependencies between nodes or variables through their topological structure, with paths between any two nodes indicating a sequential dependency on the nodes traversed. Message Passing Neural Networks (MPNNs) leverage these
Externí odkaz:
http://arxiv.org/abs/2411.12052
We report the first study using active-orbital-based and adaptive CC($P$;$Q$) approaches to describe excited electronic states. These CC($P$;$Q$) methodologies are applied, alongside their completely renormalized (CR) coupled-cluster (CC) and equatio
Externí odkaz:
http://arxiv.org/abs/2411.11245
Autor:
R, Karthik, Srivastava, Ashutosh, Midya, Soumen, Shanu, Akbar, Slathia, Surbhi, Vandana, Sajith, Sreeram, Punathil Raman, Kar, Swastik, Glavin, Nicholas R., Roy, Ajit K, Singh, Abhishek Kumar, Tiwary, Chandra Sekhar
Miniaturization of electronic components has led to overheating, increasing power consumption and causing early circuit failures. Conventional heat dissipation methods are becoming inadequate due to limited surface area and higher short-circuit risks
Externí odkaz:
http://arxiv.org/abs/2411.10030