Abu Sebastian

Electrical and Electronic Engineering · 2002–2022

A researcher in the Science corpus: 18 papers; trusts 107 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 308 citations into it; #2,370 of 16,539 by trust here, #2,493 by citations
• Artificial Intelligence — 146 citations into it; #35,226 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Materials Chemistry — 81 citations into it
• Cognitive Neuroscience — 71 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Polymers and Plastics — 32 citations into it
• Hardware and Architecture — 25 citations into it

Whom Abu Sebastian trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. M. Le Gallo
4. Onur Mutlu
5. Geoffrey W. Burr
6. Kaiming He
7. Shuangchen Li
8. Yoshua Bengio
9. Yann LeCun
10. Daniele Ielmini

Who trusts Abu Sebastian overall

1. Santosh Devasia
2. Yangmin Li
3. Sumeet S. Aphale
4. Georg Schitter
5. S. O. Reza Moheimani
6. M. Le Gallo
7. Geethan Karunaratne
8. Qingsong Xu
9. Evangelos Eleftheriou
10. Yuen Kuan Yong

Papers from Abu Sebastian's perspective

Ordered by how strongly the researchers Abu Sebastian trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Constrained Few-shot Class-incremental Learning (2022) — 204 citations
• Mixed-precision in-memory computing (2017) — 420 citations
• Neuromorphic computing using non-volatile memory (2017) — 1,066 citations
• Memory devices and applications for in-memory computing (2020) — 2,015 citations
• Neuromorphic computing with multi-memristive synapses (2017) — 751 citations
• Robust high-dimensional memory-augmented neural networks (2020) — 123 citations
• Stochastic phase-change neurons. (2016) — 980 citations
• In-memory hyperdimensional computing (2019) — 305 citations
• Accurate deep neural network inference using computational phase-change memory (2019) — 474 citations
• Recent Progress in Phase-Change Memory Technology (2016) — 336 citations
• Design methodologies for robust nano-positioning (2005) — 329 citations
• Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks (2022) — 10 citations
• Memristive technologies for data storage, computation, encryption, and radio-frequency communication (2022) — 606 citations
• In-memory computing on a photonic platform (2018) — 339 citations
• A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference (2022) — 314 citations
• 2022 roadmap on neuromorphic computing and engineering (2021) — 589 citations
• An overview of phase-change memory device physics (2020) — 383 citations
• High bandwidth nano-positioner: A robust control approach (2002) — 438 citations