Simon Kornblith
32 papers · 2019–2025 · 6 venues · across top CS/AI venues
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Machine Learning
(28)
Deep Learning
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Learning Types Β· Machine Learning
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(12)
Artificial Intelligence
(9)
Computer Vision
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Core AI Β· Computer Vision
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representation learning
(12)
transfer learning
(6)
contrastive learning
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image classification
(6)
self-supervised learning
(6)
convolutional neural network
(4)
neural network
(4)
data augmentation
(4)
zero-shot learning
(3)
neural network representation
(3)
domain adaptation
(3)
human similarity judgment
(2)
model ensemble
(2)
representational similarity
(2)
vision transformer
(2)
model merging
(2)
neural network optimization
(2)
knowledge distillation
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feature extraction
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visual representation
(2)
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?
NeurIPS 2024
Does progress on ImageNet transfer to real-world datasets?
NeurIPS 2023
FlexiViT: One Model for All Patch Sizes
CVPR 2023
Guiding Image Captioning Models Toward More Specific Captions
ICCV 2023
Human alignment of neural network representations
ICLR 2023
Hyperbolic Contrastive Learning for Visual Representations Beyond Objects
CVPR 2023
Improving neural network representations using human similarity judgments
NeurIPS 2023
On the Relationship Between Explanation and Prediction: A Causal View
ICML 2023
Scaling Forward Gradient With Local Losses
ICLR 2023
Boosting Contrastive Self-Supervised Learning With False Negative Cancellation
WACV 2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
ICML 2022
Patching open-vocabulary models by interpolating weights
NeurIPS 2022
Robust Fine-Tuning of Zero-Shot Models
CVPR 2022
Big Self-Supervised Models Advance Medical Image Classification
ICCV 2021
Do Vision Transformers See Like Convolutional Neural Networks?
NeurIPS 2021
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
ICLR 2021
Generalised Lipschitz Regularisation Equals Distributional Robustness
ICML 2021
Generalized Shape Metrics on Neural Representations
NeurIPS 2021
Meta-learning to Improve Pre-training
NeurIPS 2021
MIST: Multiple Instance Spatial Transformer
CVPR 2021
Teaching with Commentaries
ICLR 2021
Why Do Better Loss Functions Lead to Less Transferable Features?
NeurIPS 2021
A Simple Framework for Contrastive Learning of Visual Representations
ICML 2020
Big Self-Supervised Models are Strong Semi-Supervised Learners
NeurIPS 2020
Revisiting Spatial Invariance with Low-Rank Local Connectivity
ICML 2020
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
NeurIPS 2020
Do Better ImageNet Models Transfer Better?
CVPR 2019
Saccader: Improving Accuracy of Hard Attention Models for Vision
NeurIPS 2019
Similarity of Neural Network Representations Revisited
ICML 2019
When does label smoothing help?
NeurIPS 2019