Vikas Chandra

Computer Vision and Pattern Recognition · International Symposium on Computer Architecture · 2012–2021

A researcher in the Science corpus: 9 papers; trusts 277 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 142 citations into it
• Artificial Intelligence — 119 citations into it; #17,526 of 35,345 by trust here, #4,888 by citations
• Model Architectures — 50 citations into it
• Efficiency & Compression — 31 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Signal Processing — 12 citations into it; #5,223 of 7,572 by trust here, #4,201 by citations
• Speech & Audio — 5 citations into it

Whom Vikas Chandra trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Ross B. Girshick
9. Alex Graves
10. J. Urgen Schmidhuber

Who trusts Vikas Chandra overall

1. Manuele Rusci
2. Pete Warden
3. Ji Lin
4. Md Maruf Hossain Shuvo
5. Liangzhen Lai
6. Yongming Shen
7. Dan Stowell

Papers from Vikas Chandra's perspective

Ordered by how strongly the researchers Vikas Chandra 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network (2017) — 561 citations
• Improve Vision Transformers Training by Suppressing Over-smoothing (2021) — 30 citations
• Hello Edge: Keyword Spotting on Microcontrollers (2017) — 505 citations
• Federated Learning with Non-IID Data (2018) — 3,343 citations
• Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks (2016) — 535 citations
• Exploring sub-20nm FinFET design with Predictive Technology Models (2012) — 325 citations
• Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation (2021) — 245 citations
• RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing (2019) — 273 citations
• CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs (2018) — 489 citations