Manoj Plakal

Artificial Intelligence · 2010–2021

A researcher in the Science corpus: 12 papers; trusts 205 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #18,998 of 35,345 by trust here, #13,163 by citations
• Computer Vision and Pattern Recognition — 96 citations into it; #7,628 of 21,575 by trust here, #3,765 by citations
• Signal Processing — 88 citations into it; #697 of 7,572 by trust here, #141 by citations
• Representation & Self-Supervised Learning — 41 citations into it; #2,223 of 3,145 by trust here, #1,913 by citations
• Speech & Audio — 25 citations into it; #704 of 1,076 by trust here, #600 by citations
• Evaluation & Benchmarks — 22 citations into it; #1,205 of 3,147 by trust here, #643 by citations
• Optimization & Training — 19 citations into it
• Model Architectures — 17 citations into it

Whom Manoj Plakal trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Eduardo Fonseca
4. Kaiming He
5. Christian Szegedy
6. Alex Krizhevsky
7. Jimmy Ba
8. Sergey Ioffe
9. Yoshua Bengio
10. Justin Salamon

Who trusts Manoj Plakal overall

1. Eduardo Fonseca
2. Xavier Favory
3. Xavier Serra
4. David F. Harwath
5. Frederic Font
6. Luyu Wang
7. Aren Jansen
8. Janek Ebbers
9. D. Ellis
10. Qiuqiang Kong

Papers from Manoj Plakal's perspective

Ordered by how strongly the researchers Manoj Plakal trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• CNN architectures for large-scale audio classification (2016) — 3,011 citations
• Unsupervised Learning of Semantic Audio Representations (2017) — 137 citations
• Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision (2019) — 32 citations
• The Benefit of Temporally-Strong Labels in Audio Event Classification (2021) — 159 citations
• Self-Supervised Learning from Automatically Separated Sound Scenes (2021) — 13 citations
• Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases (2021) — 15 citations
• Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking (2020) — 27 citations
• Learning Sound Event Classifiers from Web Audio with Noisy Labels (2019) — 126 citations
• Audio tagging with noisy labels and minimal supervision (2019) — 103 citations
• General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline (2018) — 167 citations
• Audio Set: An ontology and human-labeled dataset for audio events (2017) — 4,118 citations
• Dapper, a Large-Scale Distributed Systems Tracing Infrastructure (2010) — 744 citations