Akshat Kumar

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 4 papers; trusts 29 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it
• Signal Processing — 37 citations into it
• Management Science and Operations Research — 35 citations into it
• Model Architectures — 21 citations into it
• Time-Series & Forecasting — 19 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Electrical and Electronic Engineering — 9 citations into it
• Reinforcement Learning — 9 citations into it

Whom Akshat Kumar trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Doina Precup
4. Geoffrey E. Hinton
5. Marco A. Wiering
6. Rob J Hyndman
7. Yoshua Bengio
8. Noam Shazeer
9. Kaiming He
10. Diederik P. Kingma

Papers from Akshat Kumar's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• ETSformer: Exponential Smoothing Transformers for Time-series Forecasting (2022) — 321 citations
• CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (2022) — 483 citations
• Unified Training of Universal Time Series Forecasting Transformers (2024) — 656 citations
• Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management (2020) — 41 citations