Sameer Bansal

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 33 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #32,089 of 35,345 by trust here, #30,347 by citations
• Speech & Audio — 20 citations into it; #961 of 1,076 by trust here, #680 by citations
• Natural Language Processing — 18 citations into it
• Signal Processing — 17 citations into it
• Model Architectures — 13 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Sameer Bansal trusts overall

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

1. Geoffrey E. Hinton
2. Ronald J. Williams
3. Sergey Ioffe
4. Sepp Hochreiter
5. Vinod Nair
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Nitish Srivastava
9. J. Urgen Schmidhuber
10. Christian Szegedy

Who trusts Sameer Bansal overall

1. Changhan Wang
2. Anne Wu
3. Matthias Sperber
4. Juan Pino
5. Mattia Antonino Di Gangi
6. Ye Jia
7. Hirofumi Inaguma
8. Jiajun Zhang
9. Elizabeth Salesky
10. Xian Li

Papers from Sameer Bansal's perspective

Ordered by how strongly the researchers Sameer Bansal 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
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

• Analyzing ASR Pretraining for Low-Resource Speech-to-Text Translation (2019) — 75 citations
• Pre-training on high-resource speech recognition improves low-resource speech-to-text translation (2018) — 216 citations