Zobrazeno 1 - 10
of 14 108
pro vyhledávání: '"Faye, P."'
Active Learning aims to optimize performance while minimizing annotation costs by selecting the most informative samples from an unlabelled pool. Traditional uncertainty sampling often leads to sampling bias by choosing similar uncertain samples. We
Externí odkaz:
http://arxiv.org/abs/2411.17444
In this article we study the spectral, linear and nonlinear stability of stationary shock profile solutions to the Lax-Wendroff scheme for hyperbolic conservation laws. We first clarify the spectral stability of such solutions depending on the convex
Externí odkaz:
http://arxiv.org/abs/2411.13094
Autor:
Kokolis, Apostolos, Kuchnik, Michael, Hoffman, John, Kumar, Adithya, Malani, Parth, Ma, Faye, DeVito, Zachary, Sengupta, Shubho, Saladi, Kalyan, Wu, Carole-Jean
Reliability is a fundamental challenge in operating large-scale machine learning (ML) infrastructures, particularly as the scale of ML models and training clusters continues to grow. Despite decades of research on infrastructure failures, the impact
Externí odkaz:
http://arxiv.org/abs/2410.21680
We present a novel deep graphical representation that seamlessly merges principles of game theory with laws of statistical mechanics. It performs feature extraction, dimensionality reduction, and pattern classification within a single learning framew
Externí odkaz:
http://arxiv.org/abs/2410.12264
Cross-modal alignment Learning integrates information from different modalities like text, image, audio and video to create unified models. This approach develops shared representations and learns correlations between modalities, enabling application
Externí odkaz:
http://arxiv.org/abs/2409.11059
Publikováno v:
ICIP 2024
Deep Neural network learning for image processing faces major challenges related to changes in distribution across layers, which disrupt model convergence and performance. Activation normalization methods, such as Batch Normalization (BN), have revol
Externí odkaz:
http://arxiv.org/abs/2409.04759
Publikováno v:
IJCNN 2024
Deep neural networks have become a staple in solving intricate problems, proving their mettle in a wide array of applications. However, their training process is often hampered by shifting activation distributions during backpropagation, resulting in
Externí odkaz:
http://arxiv.org/abs/2409.04757
Autor:
Faye, Guillaume, Seraj, Ali
Gravitational waves cause a precession in a freely falling spinning object and a net change of orientation after the passage of the wave, dubbed as the gyroscopic memory. In this paper, we will consider isolated gravitational sources in the post-Newt
Externí odkaz:
http://arxiv.org/abs/2409.02624
We give a complete expansion, at any accuracy order, for the iterated convolution of a complex valued integrable sequence in one space dimension. The remainders are estimated sharply with generalized Gaussian bounds. The result applies in probability
Externí odkaz:
http://arxiv.org/abs/2408.12876
Object detection is a fundamental challenge in computer vision, centered on recognizing objects within images, with diverse applications in areas like image analysis, robotics, and autonomous vehicles. Although existing methods have achieved great su
Externí odkaz:
http://arxiv.org/abs/2408.10787