Jonas Geiping

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 11 papers; trusts 648 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 389 citations into it; #35,054 of 35,345 by trust here, #7,531 by citations
• Computer Vision and Pattern Recognition — 312 citations into it; #21,221 of 21,575 by trust here, #6,795 by citations
• Representation & Self-Supervised Learning — 115 citations into it
• Safety, Robustness & Interpretability — 76 citations into it; #2,179 of 2,251 by trust here, #1,078 by citations
• Generative Models — 60 citations into it
• Optimization & Training — 46 citations into it
• Natural Language Processing — 45 citations into it
• Signal Processing — 45 citations into it

Whom Jonas Geiping trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Christian Szegedy
9. Ross B. Girshick
10. Karen Simonyan

Who trusts Jonas Geiping overall

1. Yangsibo Huang
2. Nuria Rodr'iguez-Barroso
3. Nguyen Binh Truong
4. Wenqi Wei
5. Fan Mo
6. John Nguyen
7. Eric Wallace
8. Georgios Kaissis
9. Mingxia Liu
10. Micah Goldblum

Papers from Jonas Geiping's perspective

Ordered by how strongly the researchers Jonas Geiping 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models (2022) — 505 citations
• Universal Guidance for Diffusion Models (2023) — 495 citations
• Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery (2023) — 425 citations
• MetaPoison: Practical General-purpose Clean-label Data Poisoning (2020) — 237 citations
• Inverting Gradients - How easy is it to break privacy in federated learning? (2020) — 1,706 citations
• A Watermark for Large Language Models (2023) — 964 citations
• Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory (2019) — 37 citations
• A Cookbook of Self-Supervised Learning (2023) — 398 citations
• Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text (2024) — 325 citations
• Baseline Defenses for Adversarial Attacks Against Aligned Language Models (2023) — 743 citations
• Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise (2022) — 440 citations