Mac Schwager

Computer Vision and Pattern Recognition · with Driess · 2023

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Mac Schwager trusts

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

  1. Kaiming He (in Computer Vision and Pattern Recognition)
  2. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  3. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  4. X. Zhang (in Computer Vision and Pattern Recognition)
  5. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  6. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  7. Jitendra Malik (in Computer Vision and Pattern Recognition)
  8. Piotr Doll'ar (in Computer Vision and Pattern Recognition)
  9. Ilya Sutskever (in Computer Vision and Pattern Recognition)

Papers from Mac Schwager's perspective

Ordered by how strongly the researchers Mac Schwager 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. Language Models are Few-Shot Learners (Neural Information Processing Systems)
  3. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
  4. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (International Conference on Learning Representations)
  5. Learning Transferable Visual Models From Natural Language Supervision (International Conference on Machine Learning)
  6. Hierarchical Text-Conditional Image Generation with CLIP Latents (arXiv.org)
  7. PaLM: Scaling Language Modeling with Pathways (Journal of machine learning research)
  8. Chain of Thought Prompting Elicits Reasoning in Large Language Models (Neural Information Processing Systems)
  9. Deep Unsupervised Learning using Nonequilibrium Thermodynamics
  10. Masked Autoencoders Are Scalable Vision Learners (Computer Vision and Pattern Recognition)

Their papers