Avishek Joey Bose
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 8 papers; trusts 719 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 249 citations into it; #13,189 of 35,345 by trust here, #12,974 by citations
- Safety, Robustness & Interpretability — 126 citations into it; #1,086 of 2,251 by trust here, #1,265 by citations
- Computer Vision and Pattern Recognition — 106 citations into it
- Signal Processing — 54 citations into it
- Natural Language Processing — 53 citations into it
- Graph Learning — 35 citations into it
- Representation & Self-Supervised Learning — 32 citations into it; #2,311 of 3,145 by trust here, #2,805 by citations
- Optimization & Training — 24 citations into it
Whom Avishek Joey Bose trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Ian J. Goodfellow
- Christian Szegedy
- Ross B. Girshick
- Ilya Sutskever
- Yann LeCun
- Kaiming He
Who trusts Avishek Joey Bose overall
- Enyan Dai
- Yang Song
- Ninareh Mehrabi
- William L. Hamilton
- Yifan Wang
- Dana Pessach
- Petar Velickovic
- Abhishek Sinha
- Chen Gao
- Suhang Wang
Papers from Avishek Joey Bose's perspective
Ordered by how strongly the researchers Avishek Joey Bose 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Adversarial Contrastive Estimation (2018) — 60 citations
- Compositional Fairness Constraints for Graph Embeddings (2019) — 301 citations
- Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding (2021) — 161 citations
- Adversarial Example Games (2020) — 57 citations
- Adversarial Attacks on Face Detectors Using Neural Net Based Constrained Optimization (2018) — 95 citations
- Online Adversarial Attacks (2021) — 13 citations
- Generalizable Adversarial Attacks Using Generative Models (2019) — 12 citations
- Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling (2019) — 7 citations