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pro vyhledávání: '"Genet, Remi"'
Autor:
Genet, Remi, Inzirillo, Hugo
We introduce a new method inspired by Adam that enhances convergence speed and achieves better loss function minima. Traditional optimizers, including Adam, apply uniform or globally adjusted learning rates across neural networks without considering
Externí odkaz:
http://arxiv.org/abs/2410.24216
Autor:
Genet, Remi, Inzirillo, Hugo
Recent research has challenged the necessity of complex deep learning architectures for time series forecasting, demonstrating that simple linear models can often outperform sophisticated approaches. Building upon this insight, we introduce a novel a
Externí odkaz:
http://arxiv.org/abs/2410.21448
Autor:
Inzirillo, Hugo, Genet, Remi
This paper introduces KAMoE, a novel Mixture of Experts (MoE) framework based on Gated Residual Kolmogorov-Arnold Networks (GRKAN). We propose GRKAN as an alternative to the traditional gating function, aiming to enhance efficiency and interpretabili
Externí odkaz:
http://arxiv.org/abs/2409.15161
Autor:
Inzirillo, Hugo, Genet, Remi
We propose a novel approach that enhances multivariate function approximation using learnable path signatures and Kolmogorov-Arnold networks (KANs). We enhance the learning capabilities of these networks by weighting the values obtained by KANs using
Externí odkaz:
http://arxiv.org/abs/2406.17890
Autor:
Genet, Remi, Inzirillo, Hugo
Capturing complex temporal patterns and relationships within multivariate data streams is a difficult task. We propose the Temporal Kolmogorov-Arnold Transformer (TKAT), a novel attention-based architecture designed to address this task using Tempora
Externí odkaz:
http://arxiv.org/abs/2406.02486
Autor:
Genet, Remi, Inzirillo, Hugo
Recurrent Neural Networks (RNNs) have revolutionized many areas of machine learning, particularly in natural language and data sequence processing. Long Short-Term Memory (LSTM) has demonstrated its ability to capture long-term dependencies in sequen
Externí odkaz:
http://arxiv.org/abs/2405.07344
Autor:
Inzirillo, Hugo, Genet, Rémi
The number of pension funds has multiplied exponentially over the last decade. Active portfolio management requires a precise analysis of the performance drivers. Several risk and performance attribution metrics have been developed since the 70s to g
Externí odkaz:
http://arxiv.org/abs/2111.06886
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