Kishore Reddy Konda
Computer Vision and Pattern Recognition · 2013–2015
A researcher in the Science corpus: 6 papers; trusts 266 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 55 citations into it; #9,970 of 21,575 by trust here, #6,873 by citations
- Artificial Intelligence — 38 citations into it
- Model Architectures — 21 citations into it
- Optimization & Training — 17 citations into it
- Representation & Self-Supervised Learning — 17 citations into it
- Signal Processing — 14 citations into it
- Natural Language Processing — 13 citations into it
- Evaluation & Benchmarks — 9 citations into it
Whom Kishore Reddy Konda trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Roland Memisevic
- Geoffrey E. Hinton
- Quoc V. Le
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Graham W. Taylor
- J. Urgen Schmidhuber
- Kaiming He
Who trusts Kishore Reddy Konda overall
- Muhamad Risqi U. Saputra
- John Flynn
- Alexey Dosovitskiy
- Dinesh Jayaraman
- Andrew Markham
- Ivan Neulander
- Shan Li
- Damien Teney
- Kristen Grauman
- William Lotter
Papers from Kishore Reddy Konda's perspective
Ordered by how strongly the researchers Kishore Reddy Konda 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Combining modality specific deep neural networks for emotion recognition in video (2013) — 397 citations
- Unsupervised learning of depth and motion (2013) — 41 citations
- How far can we go without convolution: Improving fully-connected networks (2015) — 57 citations
- EmoNets: Multimodal deep learning approaches for emotion recognition in video (2015) — 412 citations
- Recurrent Neural Networks for Emotion Recognition in Video (2015) — 393 citations
- Modeling Deep Temporal Dependencies with Recurrent "Grammar Cells" (2014) — 120 citations