Daniele Calandriello
Artificial Intelligence · 2013–2025
A researcher in the Science corpus: 6 papers; trusts 28 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 78 citations into it
- Reinforcement Learning — 35 citations into it; #1,148 of 2,242 by trust here, #1,455 by citations
- Computer Vision and Pattern Recognition — 34 citations into it
- Optimization & Training — 20 citations into it
- Computational Mechanics — 10 citations into it; #4,583 of 10,001 by trust here, #6,287 by citations
- Statistics and Probability — 10 citations into it
- Statistical Learning Theory — 8 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
Whom Daniele Calandriello trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Dimitri P. Bertsekas
- Eric R. Ziegel
- Kaiming He
- Alex Krizhevsky
- Yoshua Bengio
- Sahand N. Negahban
- X. Zhang
- Yann LeCun
- Sham M. Kakade
Who trusts Daniele Calandriello overall
- Yu Zhang
- A. Perera
- Diana Borsa
- Tingwu Wang
- Qiang Yang
- Ameya Prabhu
- Parameswaran Kamalaruban
- Thore Graepel
- Renjie Liao
Papers from Daniele Calandriello's perspective
Ordered by how strongly the researchers Daniele Calandriello trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- A General Theoretical Paradigm to Understand Learning from Human Preferences (2023) — 1,063 citations
- Sparse multi-task reinforcement learning (2014) — 80 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Information-theoretic Online Memory Selection for Continual Learning (2022) — 64 citations
- One Pass ImageNet (2021) — 21 citations
- Safe Policy Iteration (2013) — 112 citations