Moninder Singh
Artificial Intelligence · 2012–2021
A researcher in the Science corpus: 6 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 97 citations into it
- Safety, Robustness & Interpretability — 59 citations into it
- Safety Research — 29 citations into it
- Reinforcement Learning — 8 citations into it
Whom Moninder Singh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Moninder Singh overall
Papers from Moninder Singh's perspective
Ordered by how strongly the researchers Moninder Singh trusts endorse each paper. Same corpus, a different order for every researcher.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Distilling the Knowledge in a Neural Network (arXiv.org)
- Language Models are Unsupervised Multitask Learners
- RoBERTa: A Robustly Optimized BERT Pretraining Approach (arXiv.org)
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
- Equality of Opportunity in Supervised Learning
- GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
- Fairness through awareness
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- Your fairness may vary: Pretrained language model fairness in toxic text classification (2021) — 61 citations
- Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration (2018) — 25 citations
- An Analytics Approach for Proactively Combating Voluntary Attrition of Employees (2012) — 28 citations
- AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019) — 755 citations
- One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques (2019) — 478 citations
- AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias (2018) — 944 citations