Simon Kornblith
32 papers · 2019–2025 · 6 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+10 more ↓ Show less ↑
π Academic Marathon (6) π Conference Polyglot (6) π Interdisciplinary Bridge π§ Keyword Pioneer π Cross-Pollinator (6)
π
Cross-Pollinator
(6)
π
Renaissance Researcher
(6)
πΊοΈ
Taxonomy Completionist
(57)
π
Keyword Champion
(2)
π
Triple Crown
π₯
Unstoppable
(7)
β
The Questioner
(7)
π
Century Club
(32)
ποΈ
Keyword Collector
(108)
β‘
Prolific Year
(8)
Conferences
NIPS (12)
ICML (7)
CVPR (5)
ICLR (5)
ICCV (2)
WACV (1)
Top co-authors
Research topics
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