Kashif Rasul

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 4 papers; trusts 99 researchers and is trusted by 512 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #1,116 of 35,345 by trust here, #1,857 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #1,984 of 21,575 by trust here, #1,530 by citations
• Natural Language Processing — 21 citations into it; #5,570 of 5,688 by trust here, #4,250 by citations
• Generative Models — 17 citations into it
• Signal Processing — 13 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it
• Management Science and Operations Research — 9 citations into it
• Model Architectures — 9 citations into it

Whom Kashif Rasul trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Graves
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. Pascal Vincent
9. Aäron van den Oord
10. Aapo Hyvärinen

Who trusts Kashif Rasul overall

1. Fei Ye
2. Yang Song
3. Alexander Rainer Tassilo Gepperth
4. Adrian G. Bors
5. Jaehong Yoon
6. Timothée Lesort
7. Benedikt Pfülb
8. Stanislaw Jastrzebski
9. Martin Wistuba
10. Nicholas Carlini

Papers from Kashif Rasul's perspective

Ordered by how strongly the researchers Kashif Rasul 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms (2017) — 10,881 citations
• FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP (2019) — 933 citations
• Zephyr: Direct Distillation of LM Alignment (2023) — 618 citations
• Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting (2021) — 554 citations