Zobrazeno 1 - 10
of 283
pro vyhledávání: '"68T09"'
Autor:
Rieck, Bastian
This overview article makes the case for how topological concepts can enrich research in machine learning. Using the Euler Characteristic Transform (ECT), a geometrical-topological invariant, as a running example, I present different use cases that r
Externí odkaz:
http://arxiv.org/abs/2410.17760
Autor:
D'Amore, Luisa
According to the Hughes phenomenon, the major challenges encountered in computations with learning models comes from the scale of complexity, e.g. the so-called curse of dimensionality. There are various approaches for accelerate learning computation
Externí odkaz:
http://arxiv.org/abs/2410.09926
This paper introduces to a structured application of the One-Class approach and the One-Class-One-Network model for supervised classification tasks, specifically addressing a vowel phonemes classification case study within the Automatic Speech Recogn
Externí odkaz:
http://arxiv.org/abs/2410.05320
Autor:
Fossi, Gabriele, Boulaimen, Youssef, Outemzabet, Leila, Jeanray, Nathalie, Gerart, Stephane, Vachenc, Sebastien, Giemza, Joanna, Raieli, Salvatore
The advancement of artificial intelligence algorithms has expanded their application to several fields such as the biomedical domain. Artificial intelligence systems, including Large Language Models (LLMs), can be particularly advantageous in drug di
Externí odkaz:
http://arxiv.org/abs/2409.15817
Autor:
Schwerdtner, Paul, Mohan, Prakash, Pachalieva, Aleksandra, Bessac, Julie, O'Malley, Daniel, Peherstorfer, Benjamin
This work introduces an online greedy method for constructing quadratic manifolds from streaming data, designed to enable in-situ analysis of numerical simulation data on the Petabyte scale. Unlike traditional batch methods, which require all data to
Externí odkaz:
http://arxiv.org/abs/2409.02703
We investigate the ability of Diffusion Variational Autoencoder ($\Delta$VAE) with unit sphere $\mathcal{S}^2$ as latent space to capture topological and geometrical structure and disentangle latent factors in datasets. For this, we introduce a new d
Externí odkaz:
http://arxiv.org/abs/2409.01303
Autor:
Islam, Saiful, Hasan, Md. Nahid
Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment challenging. In recent years, high-throughput omic technologies have facilitated the discovery of mechan
Externí odkaz:
http://arxiv.org/abs/2408.08832
Discrimination mitigation with machine learning (ML) models could be complicated because multiple factors may interweave with each other including hierarchically and historically. Yet few existing fairness measures are able to capture the discriminat
Externí odkaz:
http://arxiv.org/abs/2408.06099
In the realm of modern mobile E-commerce, providing users with nearby commercial service recommendations through location-based online services has become increasingly vital. While machine learning approaches have shown promise in multi-scene recomme
Externí odkaz:
http://arxiv.org/abs/2408.07278
Autor:
Sun, Maojun, Han, Ruijian, Jiang, Binyan, Qi, Houduo, Sun, Defeng, Yuan, Yancheng, Huang, Jian
We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large models. LAMBDA is designed to address data analysis challenges in complex data-driven applications t
Externí odkaz:
http://arxiv.org/abs/2407.17535