Mathew Magimai-Doss
Artificial Intelligence · 2011–2015
A researcher in the Science corpus: 6 papers; trusts 81 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 48 citations into it; #4,956 of 35,345 by trust here, #8,619 by citations
- Signal Processing — 36 citations into it; #740 of 7,572 by trust here, #1,033 by citations
- Speech & Audio — 35 citations into it; #557 of 1,076 by trust here, #619 by citations
- Natural Language Processing — 28 citations into it; #2,248 of 5,688 by trust here, #3,687 by citations
- Computer Vision and Pattern Recognition — 14 citations into it
- Model Architectures — 11 citations into it
- Optimization & Training — 6 citations into it
- Probabilistic & Bayesian Methods — 6 citations into it
Whom Mathew Magimai-Doss trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- L. Eon Bottou
- Geoffrey E. Hinton
- Ronan Collobert
- Abdel-rahman Mohamed
- Yoshua Bengio
- George E. Dahl
- Alex Krizhevsky
- Honglak Lee
- Ilya Sutskever
Who trusts Mathew Magimai-Doss overall
- Dimitri Palaz
- Ronan Collobert
- Jongpil Lee
- Pegah Ghahremani
- Orestis Tsinalis
- Li Deng
- Roni Mittelman
- Neil Zeghidour
- George Trigeorgis
- Nitika Verma
Papers from Mathew Magimai-Doss's perspective
Ordered by how strongly the researchers Mathew Magimai-Doss 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks (2013) — 209 citations
- Analysis of CNN-based speech recognition system using raw speech as input (2015) — 291 citations
- Current trends in multilingual speech processing (2011) — 41 citations
- Convolutional Neural Networks-based continuous speech recognition using raw speech signal (2015) — 179 citations
- End-to-end Phoneme Sequence Recognition using Convolutional Neural Networks (2013) — 43 citations
- Analysis of MLP-Based Hierarchical Phoneme Posterior Probability Estimator (2011) — 86 citations