14 citations · 19 across the 8 of their papers we have counts for
5 papers · 1 filter
GeoHNNs: Geometric Hamiltonian Neural Networks
Amine Mohamed Aboussalah, Abdessalam Ed-dib
The fundamental laws of physics are intrinsically geometric, dictating the evolution of systems through principles of symmetry and conservation. While modern machine learning offer…
GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning
Abdessalam Ed-dib, Zhanibek Datbayev, Amine Mohamed Aboussalah
Fine-tuning large language models (LLMs) is computationally intensive because it requires updating all parameters. Low-Rank Adaptation (LoRA) improves efficiency by modifying only…
Finance-Informed Neural Network: Learning the Geometry of Option Pricing
Amine M. Aboussalah, Xuanze Li, Cheng Chi +1
We propose a Finance-Informed Neural Network (FINN) for option pricing and hedging that integrates financial theory directly into machine learning. Instead of training on observed…
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation
Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly to unseen or out-of-distribut…
Don't overfit the history -- Recursive time series data augmentation
Amine Mohamed Aboussalah, Min-Jae Kwon, Raj G Patel +2
Time series observations can be seen as realizations of an underlying dynamical system governed by rules that we typically do not know. For time series learning tasks, we need to u…