Vaishak Belle
Artificial Intelligence · 2011–2020
A researcher in the Science corpus: 24 papers; trusts 142 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 153 citations into it; #9,866 of 35,345 by trust here, #13,163 by citations
- Probabilistic & Bayesian Methods — 49 citations into it; #931 of 1,525 by trust here, #1,415 by citations
- Safety, Robustness & Interpretability — 40 citations into it; #1,096 of 2,251 by trust here, #2,205 by citations
- Computational Theory and Mathematics — 25 citations into it
- Management Science and Operations Research — 23 citations into it
- Reinforcement Learning — 19 citations into it
- Signal Processing — 19 citations into it
- Computer Vision and Pattern Recognition — 18 citations into it
Whom Vaishak Belle trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Fahiem Bacchus
- Joseph Y. Halpern
- Adnan Darwiche
- Hector J. Levesque
- Sebastian Thrun
- Stuart J. Russell
- John McCarthy
- Ronald Fagin
- Mark Chavira
- Kevin P. Murphy
Who trusts Vaishak Belle overall
- Hector J. Levesque
- Giulia Vilone
- Timo Speith
- Waddah Saeed
- Gerhard Lakemeyer
- Longo Luca
- Guy Van den Broeck
- Christian Walter Peter Omlin
- Andrea Passerini
- Gesina Schwalbe
Papers from Vaishak Belle's perspective
Ordered by how strongly the researchers Vaishak Belle trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (2015) — 122 citations
- A First-Order Logic of Probability and Only Knowing in Unbounded Domains (2016) — 19 citations
- The Symbolic Interior Point Method (2016) — 3 citations
- Semantical considerations on multiagent only knowing (2015) — 7 citations
- On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols (2011) — 11 citations
- Principles and Practice of Explainable Machine Learning (2020) — 591 citations
- Open-Universe Weighted Model Counting (2017) — 29 citations
- Weighted Model Counting With Function Symbols (2017) — 11 citations
- Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017) — 45 citations
- Solving Probability Problems in Natural Language (2017) — 22 citations
- Reasoning about Probabilities in Unbounded First-Order Dynamical Domains (2017) — 28 citations
- Component Caching in Hybrid Domains with Piecewise Polynomial Densities (2016) — 25 citations
- Hashing-Based Approximate Probabilistic Inference in Hybrid Domains (2015) — 56 citations
- Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming (2015) — 23 citations
- Sample-based abstraction for hybrid relational MDPs (2015) — 4 citations
- A Logical Theory of Localization (2015) — 9 citations
- Probabilistic Inference in Hybrid Domains by Weighted Model Integration (2015) — 123 citations
- ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains (2015) — 29 citations
- A logical theory of robot localization (2014)
- On the Progression of Knowledge in Multiagent Systems (2014)