Prasanna Sattigeri
Artificial Intelligence · 2016–2021
A researcher in the Science corpus: 13 papers; trusts 753 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 286 citations into it; #8,246 of 35,345 by trust here, #5,970 by citations
- Computer Vision and Pattern Recognition — 250 citations into it; #8,586 of 21,575 by trust here, #7,938 by citations
- Safety, Robustness & Interpretability — 79 citations into it; #1,582 of 2,251 by trust here, #1,133 by citations
- Generative Models — 77 citations into it; #498 of 1,956 by trust here, #717 by citations
- Representation & Self-Supervised Learning — 48 citations into it; #1,963 of 3,145 by trust here, #1,982 by citations
- Safety Research — 46 citations into it
- Probabilistic & Bayesian Methods — 42 citations into it; #629 of 1,525 by trust here, #633 by citations
- Model Architectures — 36 citations into it
Whom Prasanna Sattigeri trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Ian J. Goodfellow
- Ilya Sutskever
- Alex Graves
- Radford M. Neal
Who trusts Prasanna Sattigeri overall
- Francesco Locatello
- Vikram V. Ramaswamy
- Divya Saxena
- Simon Caton
- Angelina Wang
- Raphael Suter
- Sina Baharlouei
- Laleh Seyyed-Kalantari
- Yanchao Yang
- Alexander Amini
Papers from Prasanna Sattigeri's perspective
Ordered by how strongly the researchers Prasanna Sattigeri 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)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Variational Inference of Disentangled Latent Concepts from Unlabeled Observations (2017) — 602 citations
- AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition (2021) — 70 citations
- AR-Net: Adaptive Frame Resolution for Efficient Action Recognition (2020) — 177 citations
- Fairness GAN (2018) — 98 citations
- Understanding Unequal Gender Classification Accuracy from Face Images (2018) — 53 citations
- not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget (2020) — 10 citations
- Co-regularized Alignment for Unsupervised Domain Adaptation (2018) — 167 citations
- Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2020) — 336 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
- Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference (2017) — 161 citations
- TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning (2016) — 44 citations