Guy Van den Broeck

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 8 papers; trusts 39 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #19,491 of 35,345 by trust here, #15,236 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Signal Processing — 17 citations into it
• Management Science and Operations Research — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Computer Networks and Communications — 12 citations into it
• Model Architectures — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it

Whom Guy Van den Broeck trusts overall

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

1. Adnan Darwiche
2. Scott M. Lundberg
3. Mark Chavira
4. Tian Sang
5. Clark W. Barrett
6. Steffen Rendle
7. Andrew Y. Ng
8. Daphne Koller
9. Erik Strumbelj
10. Vaishak Belle

Who trusts Guy Van den Broeck overall

1. Vaishak Belle
2. Luc De Raedt
3. Robin Manhaeve
4. Angelika Kimmig

Papers from Guy Van den Broeck's perspective

Ordered by how strongly the researchers Guy Van den Broeck trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• An Algebraic Prolog for Reasoning about Possible Worlds (2011) — 69 citations
• On the Paradox of Learning to Reason from Data (2022) — 145 citations
• On the Tractability of SHAP Explanations (2020) — 523 citations
• A Semantic Loss Function for Deep Learning with Symbolic Knowledge (2017) — 549 citations
• Component Caching in Hybrid Domains with Piecewise Polynomial Densities (2016) — 25 citations
• Hashing-Based Approximate Probabilistic Inference in Hybrid Domains (2015) — 56 citations
• Probabilistic Inference in Hybrid Domains by Weighted Model Integration (2015) — 123 citations
• ProbLog2: Probabilistic Logic Programming (2015) — 60 citations