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ORCIDHesham Mostafa
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2020 – today
- 2024
[c13]Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, Zhaocheng Zhu:
Towards Foundation Models for Knowledge Graph Reasoning. ICLR 2024- [c12]Prianka Sengupta
, Aakash Tyagi, Jiang Hu, Vivek K. Rajan, Hesham Mostafa, Somdeb Majumdar:
MinBLoG: Minimization of Boolean Logic Functions using Graph Attention Network. MLCAD 2024: 24:1-24:8 - [i23]Uday Mallappa, Hesham Mostafa, Michael Galkin, Mariano Phielipp, Somdeb Majumdar:
FloorSet - a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs. CoRR abs/2405.05480 (2024) - [i22]Hesham Mostafa, Uday Mallappa, Mikhail Galkin, Mariano Phielipp, Somdeb Majumdar:
PARSAC: Fast, Human-quality Floorplanning for Modern SoCs with Complex Design Constraints. CoRR abs/2405.05495 (2024) - [i21]Jianan Zhao, Hesham Mostafa, Mikhail Galkin, Michael M. Bronstein, Zhaocheng Zhu, Jian Tang:
GraphAny: A Foundation Model for Node Classification on Any Graph. CoRR abs/2405.20445 (2024) - [i20]Juan Cerviño, Md Asadullah Turja, Hesham Mostafa, Nageen Himayat, Alejandro Ribeiro:
Distributed Training of Large Graph Neural Networks with Variable Communication Rates. CoRR abs/2406.17611 (2024) - 2023
- [c11]Lin Meng, Hesham Mostafa, Marcel Nassar, Xiaonan Zhang, Jiawei Zhang:
Generative Graph Augmentation for Minority Class in Fraud Detection. CIKM 2023: 4200-4204 - [c10]Shengyu Feng, Hesham Mostafa, Marcel Nassar, Somdeb Majumdar, Subarna Tripathi:
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs. WACV 2023: 5119-5128 - [i19]Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, Zhaocheng Zhu:
Towards Foundation Models for Knowledge Graph Reasoning. CoRR abs/2310.04562 (2023) - [i18]Hesham Mostafa, Adam Grabowski, Md Asadullah Turja, Juan Cerviño, Alejandro Ribeiro, Nageen Himayat:
FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs. CoRR abs/2311.17847 (2023) - 2022
- [c9]Hesham Mostafa:
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs. MLSys 2022 - 2021
- [c8]Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan:
Implicit SVD for Graph Representation Learning. NeurIPS 2021: 8419-8431 - [i17]Hesham Mostafa, Marcel Nassar, Somdeb Majumdar:
On Local Aggregation in Heterophilic Graphs. CoRR abs/2106.03213 (2021) - [i16]Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan:
Implicit SVD for Graph Representation Learning. CoRR abs/2111.06312 (2021) - [i15]Hesham Mostafa:
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs. CoRR abs/2111.06483 (2021) - [i14]Shengyu Feng, Subarna Tripathi, Hesham Mostafa, Marcel Nassar, Somdeb Majumdar:
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs. CoRR abs/2112.09828 (2021) - 2020
- [i13]Hesham Mostafa, Marcel Nassar:
Permutohedral-GCN: Graph Convolutional Networks with Global Attention. CoRR abs/2003.00635 (2020) - [i12]Souvik Kundu, Hesham Mostafa, Sharath Nittur Sridhar, Sairam Sundaresan:
Attention-based Image Upsampling. CoRR abs/2012.09904 (2020)
2010 – 2019
- 2019
- [j6]Emre O. Neftci, Hesham Mostafa, Friedemann Zenke:
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36(6): 51-63 (2019) - [j5]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 Delbrück:
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps. IEEE Trans. Neural Networks Learn. Syst. 30(3): 644-656 (2019) - [c7]Mark D. McDonnell, Hesham Mostafa, Runchun Wang, André van Schaik:
Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems. ACIRS 2019: 197-204 - [c6]Hesham Mostafa, Xin Wang:
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. ICML 2019: 4646-4655 - [i11]Emre O. Neftci, Hesham Mostafa, Friedemann Zenke:
Surrogate Gradient Learning in Spiking Neural Networks. CoRR abs/1901.09948 (2019) - [i10]Hesham Mostafa, Xin Wang:
Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization. CoRR abs/1902.05967 (2019) - [i9]Mark D. McDonnell, Hesham Mostafa, Runchun Wang, André van Schaik:
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems. CoRR abs/1907.06916 (2019) - [i8]Hesham Mostafa:
Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters. CoRR abs/1912.13075 (2019) - 2018
- [j4]Hesham Mostafa, Gert Cauwenberghs:
A Learning Framework for Winner-Take-All Networks with Stochastic Synapses. Neural Comput. 30(6) (2018) - [j3]Hesham Mostafa:
Supervised Learning Based on Temporal Coding in Spiking Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 29(7): 3227-3235 (2018) - [c5]Bruno U. Pedroni, Sadique Sheik, Hesham Mostafa, Somnath Paul, Charles Augustine, Gert Cauwenberghs:
Small-footprint Spiking Neural Networks for Power-efficient Keyword Spotting. BioCAS 2018: 1-4 - [i7]Jacques Kaiser, Hesham Mostafa, Emre Neftci:
Synaptic Plasticity Dynamics for Deep Continuous Local Learning. CoRR abs/1811.10766 (2018) - 2017
- [i6]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 Delbrück:
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps. CoRR abs/1706.01406 (2017) - [i5]Hesham Mostafa, Gert Cauwenberghs:
A learning framework for winner-take-all networks with stochastic synapses. CoRR abs/1708.04251 (2017) - [i4]Hesham Mostafa, Vishwajith Ramesh, Gert Cauwenberghs:
Deep supervised learning using local errors. CoRR abs/1711.06756 (2017) - 2016
- [c4]Hesham Mostafa, Christian Mayr, Giacomo Indiveri:
Beyond spike-timing dependent plasticity in memristor crossbar arrays. ISCAS 2016: 926-929 - [i3]Hesham Mostafa:
Supervised learning based on temporal coding in spiking neural networks. CoRR abs/1606.08165 (2016) - 2015
- [j2]Hesham Mostafa, Lorenz K. Müller, Giacomo Indiveri:
Rhythmic Inhibition Allows Neural Networks to Search for Maximally Consistent States. Neural Comput. 27(12): 2510-2547 (2015) - [i2]Hesham Mostafa, Lorenz K. Müller, Giacomo Indiveri:
An event-based architecture for solving constraint satisfaction problems. CoRR abs/1505.01139 (2015) - [i1]Hesham Mostafa, Giacomo Indiveri:
Stochastic Interpretation of Quasi-periodic Event-based Systems. CoRR abs/1512.02930 (2015) - 2014
- [j1]Hesham Mostafa, Giacomo Indiveri:
Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits. Neural Comput. 26(9): 1973-2004 (2014) - [c3]Hesham Mostafa, Federico Corradi, Fabio Stefanini, Giacomo Indiveri:
A hybrid analog/digital Spike-Timing Dependent Plasticity learning circuit for neuromorphic VLSI multi-neuron architectures. ISCAS 2014: 854-857 - 2013
- [c2]Hesham Mostafa, Federico Corradi, Marc Osswald, Giacomo Indiveri:
Automated synthesis of asynchronous event-based interfaces for neuromorphic systems. ECCTD 2013: 1-4 - [c1]Hesham Mostafa, Lorenz K. Müller, Giacomo Indiveri:
Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems. NIPS 2013: 719-727
Coauthor Index
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