14 citations · 19 across the 8 of their papers we have counts for
9 papers
Quantum Geometric Tensor Preconditioning for Stable Training of Recurrent Neural Quantum States
Adil Attar, Amine M. Aboussalah, Mohamed Hibat-Allah
Neural Quantum States (NQS) provide a powerful neural network-based variational framework for representing many-body wave functions and solving for ground states. Recurrent Neural…
Geometry-Induced Long-Range Correlations in Recurrent Neural Network Quantum States
Asif Bin Ayub, Amine Mohamed Aboussalah, Mohamed Hibat-Allah
Neural Quantum States based on autoregressive recurrent neural network (RNN) wave functions enable efficient sampling without Markov-chain autocorrelation, but standard RNN archite…
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…
Are GNNs doomed by the topology of their input graph?
Amine Mohamed Aboussalah, Abdessalam Ed-dib
Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, the influence of the input graph's topology on GNN behavior remai…
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…
A Deep Reinforcement Learning Framework For Column Generation
Cheng Chi, Amine Mohamed Aboussalah, Elias B. Khalil +2
Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns). CG is the workhorse for tackling large-sca…