Soma Biswas
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 4 papers; trusts 585 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 125 citations into it; #24,144 of 35,345 by trust here, #32,362 by citations
- Computer Vision and Pattern Recognition — 119 citations into it; #14,939 of 21,575 by trust here, #21,103 by citations
- Meta & Continual Learning — 51 citations into it; #747 of 1,041 by trust here, #1,004 by citations
- Representation & Self-Supervised Learning — 43 citations into it
- Radiology, Nuclear Medicine and Imaging — 23 citations into it
- Optimization & Training — 14 citations into it
- Efficiency & Compression — 9 citations into it
- Model Architectures — 9 citations into it
Whom Soma Biswas trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- Ross B. Girshick
- Diederik P. Kingma
- Yann LeCun
- Karen Simonyan
- Andrew Zisserman
Who trusts Soma Biswas overall
Papers from Soma Biswas's perspective
Ordered by how strongly the researchers Soma Biswas trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning (2023) — 61 citations
- Spacing Loss for Discovering Novel Categories (2022) — 18 citations
- Novel Class Discovery without Forgetting (2022) — 61 citations
- A Novel Self-Supervised Re-labeling Approach for Training with Noisy Labels (2019) — 29 citations