← Authors

Simon Kornblith

32 papers · 2019–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (6) 🐝 Cross-Pollinator (6) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (57) πŸ† Keyword Champion (2) πŸ‘‘ Triple Crown πŸ”₯ Unstoppable (7) ❓ The Questioner (7) πŸ’Ž Century Club (32) πŸ—ƒοΈ Keyword Collector (108) ⚑ Prolific Year (8)

Conferences

NIPS (12) ICML (7) CVPR (5) ICLR (5) ICCV (2) WACV (1)

Top co-authors

Ting Chen (5) Mohammad Norouzi (5) Mitchell Wortsman (4) Geoffrey Hinton (4) Ludwig Schmidt (4) Lukas Muttenthaler (4) Ali Farhadi (3) Jonas Dippel (3) Lorenz Linhardt (3) Gabriel Ilharco (3)

Research topics

Core AI (1)

Keywords

representation learning (9) image classification (6) transfer learning (6) self-supervised learning (5) contrastive learning (5) convolutional neural network (4) zero-shot learning (3) neural network (3) domain adaptation (3) data augmentation (3) human similarity judgment (2) neural network representation (2) vision transformer (2) canonical correlation analysis (2) knowledge distillation (2) visual representation (2) feature extraction (2) model merging (2) model ensemble (2) domain generalization (1)

Papers

Objective drives the consistency of representational similarity across datasets ICML 2025
Small-scale proxies for large-scale Transformer training instabilities ICLR 2024
When does perceptual alignment benefit vision representations? NIPS 2024
Human alignment of neural network representations ICLR 2023
Scaling Forward Gradient With Local Losses ICLR 2023
Guiding Image Captioning Models Toward More Specific Captions ICCV 2023
Hyperbolic Contrastive Learning for Visual Representations Beyond Objects CVPR 2023
Does progress on ImageNet transfer to real-world datasets? NIPS 2023
Improving neural network representations using human similarity judgments NIPS 2023
FlexiViT: One Model for All Patch Sizes CVPR 2023
On the Relationship Between Explanation and Prediction: A Causal View ICML 2023
Boosting Contrastive Self-Supervised Learning With False Negative Cancellation WACV 2022
Patching open-vocabulary models by interpolating weights NIPS 2022
Robust Fine-Tuning of Zero-Shot Models CVPR 2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time ICML 2022
Generalised Lipschitz Regularisation Equals Distributional Robustness ICML 2021
Do Vision Transformers See Like Convolutional Neural Networks? NIPS 2021
Generalized Shape Metrics on Neural Representations NIPS 2021
Why Do Better Loss Functions Lead to Less Transferable Features? NIPS 2021
Big Self-Supervised Models Advance Medical Image Classification ICCV 2021
MIST: Multiple Instance Spatial Transformer CVPR 2021
Meta-learning to Improve Pre-training NIPS 2021
Teaching with Commentaries ICLR 2021
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth ICLR 2021
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks NIPS 2020
Big Self-Supervised Models are Strong Semi-Supervised Learners NIPS 2020
Revisiting Spatial Invariance with Low-Rank Local Connectivity ICML 2020
A Simple Framework for Contrastive Learning of Visual Representations ICML 2020
When does label smoothing help? NIPS 2019
Similarity of Neural Network Representations Revisited ICML 2019
Saccader: Improving Accuracy of Hard Attention Models for Vision NIPS 2019
Do Better ImageNet Models Transfer Better? CVPR 2019