Josip Djolonga

Computer Vision and Pattern Recognition · 2013–2025

A researcher in the Science corpus: 14 papers; trusts 681 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 439 citations into it; #20,110 of 21,575 by trust here, #2,814 by citations
• Artificial Intelligence — 394 citations into it; #24,107 of 35,345 by trust here, #2,953 by citations
• Representation & Self-Supervised Learning — 168 citations into it; #2,965 of 3,145 by trust here, #856 by citations
• Evaluation & Benchmarks — 111 citations into it
• Generative Models — 59 citations into it
• Optimization & Training — 53 citations into it
• Safety, Robustness & Interpretability — 49 citations into it
• Probabilistic & Bayesian Methods — 37 citations into it

Whom Josip Djolonga trusts overall

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

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

Who trusts Josip Djolonga overall

1. Kirthevasan Kandasamy
2. Xiaohua Zhai
3. Alexander Kolesnikov
4. Mostafa Dehghani
5. Zi Wang
6. Anders Andreassen
7. Yao-Hung Hubert Tsai
8. Gabriele Prato
9. Ali Borji
10. Bernhard Schölkopf

Papers from Josip Djolonga's perspective

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

Their papers

• Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
• A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark (2019) — 558 citations
• On Mutual Information Maximization for Representation Learning (2019) — 567 citations
• Self-Supervised Learning of Video-Induced Visual Invariances (2019) — 67 citations
• On Robustness and Transferability of Convolutional Neural Networks (2020) — 174 citations
• PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
• Practical and Consistent Estimation of f-Divergences (2019) — 52 citations
• Revisiting the Calibration of Modern Neural Networks (2021) — 562 citations
• Fast Differentiable Sorting and Ranking (2020) — 300 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Precision-Recall Curves Using Information Divergence Frontiers (2019) — 40 citations
• The Visual Task Adaptation Benchmark (2019) — 129 citations
• High-Dimensional Gaussian Process Bandits (2013) — 189 citations