6 citations · 10 across the 10 of their papers we have counts for
11 papers
LEMs: A Primer On Large Execution Models
Remi Genet, Hugo Inzirillo
This paper introduces Large Execution Models (LEMs), a novel deep learning framework that extends transformer-based architectures to address complex execution problems with flexibl…
SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms
Rémi Genet, Hugo Inzirillo
In this paper, we propose a novel approach that enhances recurrent neural networks (RNNs) by incorporating path signatures into their gating mechanisms. Our method modifies both Lo…
STAN: Smooth Transition Autoregressive Networks
Hugo Inzirillo, Remi Genet
Traditional Smooth Transition Autoregressive (STAR) models offer an effective way to model these dynamics through smooth regime changes based on specific transition variables. In t…
Keras Sig: Efficient Path Signature Computation on GPU in Keras 3
Rémi Genet, Hugo Inzirillo
In this paper we introduce Keras Sig a high-performance pythonic library designed to compute path signature for deep learning applications. Entirely built in Keras 3, \textit{Keras…
CaAdam: Improving Adam optimizer using connection aware methods
Remi Genet, Hugo Inzirillo
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 global…
A Temporal Linear Network for Time Series Forecasting
Remi Genet, Hugo Inzirillo
Recent research has challenged the necessity of complex deep learning architectures for time series forecasting, demonstrating that simple linear models can often outperform sophis…
A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts
Hugo Inzirillo, Remi Genet
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 tradit…
Deep State Space Recurrent Neural Networks for Time Series Forecasting
Hugo Inzirillo
We explore various neural network architectures for modeling the dynamics of the cryptocurrency market. Traditional linear models often fall short in accurately capturing the uniqu…
A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting
Remi Genet, Hugo Inzirillo
Capturing complex temporal patterns and relationships within multivariate data streams is a difficult task. We propose the Temporal Kolmogorov-Arnold Transformer (TKAT), a novel at…
SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series
Hugo Inzirillo, Remi Genet
We propose a novel approach that enhances multivariate function approximation using learnable path signatures and Kolmogorov-Arnold networks (KANs). We enhance the learning capabil…
Performance vs Persistence : Assess the alpha to identify outperformers
Hugo Inzirillo, Rémi Genet
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…