Micah Goldblum

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 19 papers; trusts 1,061 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 638 citations into it
• Computer Vision and Pattern Recognition — 454 citations into it
• Representation & Self-Supervised Learning — 166 citations into it
• Safety, Robustness & Interpretability — 145 citations into it
• Optimization & Training — 81 citations into it
• Signal Processing — 75 citations into it
• Generative Models — 70 citations into it
• Model Architectures — 67 citations into it

Whom Micah Goldblum trusts overall

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

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

Who trusts Micah Goldblum overall

1. Naveed Akhtar
2. Anqi Mao
3. Jonas Geiping
4. Léo Grinsztajn
5. Liam H. Fowl
6. Ajmal Saeed Mian
7. Mehryar Mohri
8. Vadim Borisov
9. Noah Hollmann
10. Tiago Pimentel

Papers from Micah Goldblum's perspective

Ordered by how strongly the researchers Micah Goldblum trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks (2021) — 115 citations
• Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models (2022) — 505 citations
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses (2020) — 406 citations
• Universal Guidance for Diffusion Models (2023) — 495 citations
• SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training (2021) — 556 citations
• Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks (2020) — 83 citations
• Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery (2023) — 425 citations
• Adversarially Robust Distillation (2019) — 270 citations
• Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory (2019) — 37 citations
• A Cookbook of Self-Supervised Learning (2023) — 398 citations
• LiveBench: A Challenging, Contamination-Free LLM Benchmark (2024) — 317 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
• When Do Neural Nets Outperform Boosted Trees on Tabular Data? (2023) — 356 citations
• Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise (2022) — 440 citations
• The Intrinsic Dimension of Images and Its Impact on Learning (2021) — 413 citations
• Adversarially Robust Few-Shot Learning: A Meta-Learning Approach (2019) — 103 citations
• Understanding Generalization through Visualizations (2019) — 93 citations
• Robust Few-Shot Learning with Adversarially Queried Meta-Learners (2019) — 14 citations