Tatsuya Konishi

Artificial Intelligence · 2022–2023

A researcher in the Science corpus: 4 papers; trusts 551 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 226 citations into it
• Computer Vision and Pattern Recognition — 148 citations into it
• Meta & Continual Learning — 131 citations into it
• Representation & Self-Supervised Learning — 57 citations into it
• Natural Language Processing — 25 citations into it
• Optimization & Training — 24 citations into it
• Radiology, Nuclear Medicine and Imaging — 21 citations into it
• Reinforcement Learning — 20 citations into it

Whom Tatsuya Konishi trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Karen Simonyan
10. Christian Szegedy

Who trusts Tatsuya Konishi overall

1. Gyuhak Kim

Papers from Tatsuya Konishi's perspective

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

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

Their papers

• Open-World Continual Learning: Unifying Novelty Detection and Continual Learning (2023) — 34 citations
• Learnability and Algorithm for Continual Learning (2023) — 37 citations
• Continual Pre-training of Language Models (2023) — 242 citations
• A Theoretical Study on Solving Continual Learning (2022) — 111 citations