Jack Lanchantin

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 316 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 325 citations into it; #21,355 of 35,345 by trust here, #5,495 by citations
• Molecular Biology — 310 citations into it
• Computer Vision and Pattern Recognition — 168 citations into it
• Safety, Robustness & Interpretability — 65 citations into it; #1,401 of 2,251 by trust here, #1,371 by citations
• Natural Language Processing — 59 citations into it
• Model Architectures — 49 citations into it
• Biophysics — 45 citations into it
• Materials Chemistry — 40 citations into it

Whom Jack Lanchantin trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ilya Sutskever
6. Alex Graves
7. Kaiming He
8. Yann LeCun
9. Jimmy Ba
10. Christian Szegedy

Who trusts Jack Lanchantin overall

1. Yonatan Belinkov
2. Travers Ching
3. Siddhant Garg
4. Di Jin
5. Ribana Roscher
6. W. Zhang
7. Shashank Singh
8. Shuhuai Ren
9. Nadir Durrani
10. Linyang Li

Papers from Jack Lanchantin's perspective

Ordered by how strongly the researchers Jack Lanchantin 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Opportunities and obstacles for deep learning in biology and medicine (2017) — 2,030 citations
• Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers (2018) — 869 citations
• Neural Message Passing for Multi-Label Classification (2019) — 41 citations
• Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks (2016) — 136 citations
• Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin (2017) — 100 citations
• General Multi-label Image Classification with Transformers (2020) — 357 citations
• Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction (2016) — 3 citations
• DeepChrome: deep-learning for predicting gene expression from histone modifications (2016)