Muhammad Sajjad

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 92 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it
• Artificial Intelligence — 27 citations into it
• Experimental and Cognitive Psychology — 11 citations into it
• Model Architectures — 11 citations into it
• Signal Processing — 10 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Muhammad Sajjad trusts overall

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

1. Diederik P. Kingma
2. Karen Simonyan
3. Alex Krizhevsky
4. Kaiming He
5. Geoffrey E. Hinton
6. Andrew Zisserman
7. Ilya Sutskever
8. Alex Graves
9. Yoshua Bengio
10. Jimmy Ba

Who trusts Muhammad Sajjad overall

1. Khan Muhammad
2. Yassine Himeur
3. Taiba Maijd Wani
4. Salman Khan

Papers from Muhammad Sajjad's perspective

Ordered by how strongly the researchers Muhammad Sajjad 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Multi-grade brain tumor classification using deep CNN with extensive data augmentation (2019) — 782 citations
• Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features (2018) — 672 citations
• Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM (2020) — 325 citations
• A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting (2020) — 430 citations