Automated synthesis of asynchronous event-based interfaces for neuromorphic systems. - Hesham Mostafa; Federico Corradi; Marc Osswald; Giacomo Indiveri (ECCTD, 2013)

Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems. - Hesham Mostafa; Lorenz K. Müller; Giacomo Indiveri (NIPS, 2013)

A hybrid analog/digital Spike-Timing Dependent Plasticity learning circuit for neuromorphic VLSI multi-neuron architectures. - Hesham Mostafa; Federico Corradi; Fabio Stefanini; Giacomo Indiveri (ISCAS, 2014)

Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits. - Hesham Mostafa; Giacomo Indiveri (Neural Comput., 2014)

Stochastic Interpretation of Quasi-periodic Event-based Systems. - Hesham Mostafa; Giacomo Indiveri (CoRR, 2015)

An event-based architecture for solving constraint satisfaction problems. - Hesham Mostafa; Lorenz K. Müller; Giacomo Indiveri (CoRR, 2015)

Rhythmic Inhibition Allows Neural Networks to Search for Maximally Consistent States. - Hesham Mostafa; Lorenz K. Müller; Giacomo Indiveri (Neural Comput., 2015)

Beyond spike-timing dependent plasticity in memristor crossbar arrays. - Hesham Mostafa; Christian Mayr 001; Giacomo Indiveri (ISCAS, 2016)

Deep supervised learning using local errors. - Hesham Mostafa; Vishwajith Ramesh; Gert Cauwenberghs (CoRR, 2017)

Small-footprint Spiking Neural Networks for Power-efficient Keyword Spotting. - Bruno U. Pedroni; Sadique Sheik; Hesham Mostafa; Somnath Paul; Charles Augustine; Gert Cauwenberghs (BioCAS, 2018)

Synaptic Plasticity Dynamics for Deep Continuous Local Learning. - Jacques Kaiser; Hesham Mostafa; Emre Neftci (CoRR, 2018)

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses. - Hesham Mostafa; Gert Cauwenberghs (Neural Comput., 2018)

Supervised Learning Based on Temporal Coding in Spiking Neural Networks. - Hesham Mostafa (IEEE Trans. Neural Networks Learn. Syst., 2018)

Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems. - Mark D. McDonnell; Hesham Mostafa; Runchun Wang; André van Schaik (ACIRS, 2019)

Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. - Hesham Mostafa; Xin Wang (ICML, 2019)

Surrogate Gradient Learning in Spiking Neural Networks. - Emre O. Neftci; Hesham Mostafa; Friedemann Zenke (CoRR, 2019)

Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters. - Hesham Mostafa (CoRR, 2019)

Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks. - Emre O. Neftci; Hesham Mostafa; Friedemann Zenke (IEEE Signal Process. Mag., 2019)

NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps. - Alessandro Aimar; Hesham Mostafa; Enrico Calabrese; Antonio Rios-Navarro; Ricardo Tapiador-Morales; Iulia-Alexandra Lungu; Moritz B. Milde; Federico Corradi; Alejandro Linares-Barranco; Shih-Chii Liu; Tobi Delbruck (IEEE Trans. Neural Networks Learn. Syst., 2019)

Permutohedral-GCN: Graph Convolutional Networks with Global Attention. - Hesham Mostafa; Marcel Nassar (CoRR, 2020)

Attention-based Image Upsampling. - Souvik Kundu 002; Hesham Mostafa; Sharath Nittur Sridhar; Sairam Sundaresan (CoRR, 2020)

Implicit SVD for Graph Representation Learning. - Sami Abu-El-Haija; Hesham Mostafa; Marcel Nassar; Valentino Crespi; Greg Ver Steeg; Aram Galstyan (NeurIPS, 2021)

On Local Aggregation in Heterophilic Graphs. - Hesham Mostafa; Marcel Nassar; Somdeb Majumdar (CoRR, 2021)

Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs. - Hesham Mostafa (MLSys, 2022)

Generative Graph Augmentation for Minority Class in Fraud Detection. - Lin Meng; Hesham Mostafa; Marcel Nassar; Xiaonan Zhang 001; Jiawei Zhang 001 (CIKM, 2023)

Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs. - Shengyu Feng; Hesham Mostafa; Marcel Nassar; Somdeb Majumdar; Subarna Tripathi (WACV, 2023)

FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs. - Hesham Mostafa; Adam Grabowski; Md Asadullah Turja; Juan Cerviño; Alejandro Ribeiro; Nageen Himayat (CoRR, 2023)

FloorSet - a VLSI Floorplanning Dataset with Design Constraints of Real-World SOCs. - Uday Mallappa; Hesham Mostafa; Mikhail Galkin 001; Mariano Phielipp; Somdeb Majumdar (ICCAD, 2024)

Towards Foundation Models for Knowledge Graph Reasoning. - Mikhail Galkin 001; Xinyu Yuan; Hesham Mostafa; Jian Tang 005; Zhaocheng Zhu (ICLR, 2024)

MinBLoG: Minimization of Boolean Logic Functions using Graph Attention Network. - Prianka Sengupta; Aakash Tyagi; Jiang Hu; Vivek K. Rajan; Hesham Mostafa; Somdeb Majumdar (MLCAD, 2024)

PARSAC: Fast, Human-quality Floorplanning for Modern SoCs with Complex Design Constraints. - Hesham Mostafa; Uday Mallappa; Mikhail Galkin 001; Mariano Phielipp; Somdeb Majumdar (CoRR, 2024)

GraphAny: A Foundation Model for Node Classification on Any Graph. - Jianan Zhao 002; Hesham Mostafa; Mikhail Galkin 001; Michael M. Bronstein; Zhaocheng Zhu; Jian Tang 005 (CoRR, 2024)

Fully-inductive Node Classification on Arbitrary Graphs. - Jianan Zhao 002; Zhaocheng Zhu; Mikhail Galkin 001; Hesham Mostafa; Michael M. Bronstein; Jian Tang 005 (ICLR, 2025)

Distributed Training of Large Graph Neural Networks With Variable Communication Rates. - Juan Cerviño; Md Asadullah Turja; Hesham Mostafa; Nageen Himayat; Alejandro Ribeiro (IEEE Trans. Signal Inf. Process. over Networks, 2025)

Early Attentive Sparsification Accelerates Neural Speech Transcription. - Zifei Xu; Sayeh Sharify; Hesham Mostafa; Tristan Webb; Wanzin Yazar; Xin Wang (CoRR, 2025)