Sarath Sreedharan
Artificial Intelligence · 2015–2023
A researcher in the Science corpus: 13 papers; trusts 47 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 129 citations into it; #13,506 of 35,345 by trust here, #10,430 by citations
- Computer Vision and Pattern Recognition — 50 citations into it; #18,301 of 21,575 by trust here, #15,162 by citations
- Reinforcement Learning — 36 citations into it
- LLM Agents & Tool Use — 24 citations into it
- Natural Language Processing — 15 citations into it
- Social Psychology — 15 citations into it
- Control and Systems Engineering — 13 citations into it
- Knowledge Graphs & Representation — 12 citations into it
Whom Sarath Sreedharan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tania Lombrozo
- Tathagata Chakraborti
- Nils J. Nilsson
- Tim Miller
- C. E. Bartlett
- Robin Roberson Murphy
- Subbarao Kambhampati
- Maria Fox
- Malte Helmert
- Daniel Bryce
Who trusts Sarath Sreedharan overall
- Subbarao Kambhampati
- Tathagata Chakraborti
- Diego Aineto
- Daniel S. Weld
- Sergio Jiménez
- Lin Guan
- Eva Onaindia
- Tim Miller
- Ilia Stepin
- Gagan Bansal
Papers from Sarath Sreedharan's perspective
Ordered by how strongly the researchers Sarath Sreedharan 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
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Et al
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change (2022) — 438 citations
- Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations (2018) — 20 citations
- Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning (2023) — 316 citations
- On the Planning Abilities of Large Language Models - A Critical Investigation (2023) — 455 citations
- Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems (2021) — 56 citations
- GPT3-to-plan: Extracting plans from text using GPT-3 (2021) — 49 citations
- Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation (2018) — 71 citations
- Balancing Explicability and Explanation in Human-Aware Planning (2017) — 45 citations
- Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy (2017) — 349 citations
- Robust planning with incomplete domain models (2017) — 38 citations
- RADAR - A Proactive Decision Support System for Human-in-the-Loop Planning (2017) — 42 citations
- Plan explicability and predictability for robot task planning (2015) — 184 citations
- Capability Models and Their Applications in Planning (2015) — 21 citations