Erik Marchi
Artificial Intelligence · 2013–2021
A researcher in the Science corpus: 8 papers; trusts 42 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 78 citations into it; #25,678 of 35,345 by trust here, #8,525 by citations
- Signal Processing — 52 citations into it; #5,620 of 7,572 by trust here, #1,120 by citations
- Speech & Audio — 35 citations into it
- Computer Vision and Pattern Recognition — 15 citations into it
- Experimental and Cognitive Psychology — 13 citations into it; #6,703 of 9,731 by trust here, #3,491 by citations
- Model Architectures — 10 citations into it
- Optimization & Training — 9 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
Whom Erik Marchi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sepp Hochreiter
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- J. Urgen Schmidhuber
- Alex Graves
- Alex Krizhevsky
- Yann LeCun
- Jimmy Ba
- Ilya Sutskever
Who trusts Erik Marchi overall
- Gioele Ciaparrone
- Xu Tan
- Leila Arras
- J. Urgen Schmidhuber
- José Antonio Arjona-Medina
- Björn Schuller
- Erica Cooper
- Dipjyoti Paul
- Mirco Ravanelli
Papers from Erik Marchi's perspective
Ordered by how strongly the researchers Erik Marchi 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
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Multi-resolution linear prediction based features for audio onset detection with bidirectional LSTM neural networks (2014) — 93 citations
- Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network (2016) — 895 citations
- Whispered and Lombard Neural Speech Synthesis (2021) — 18 citations
- Generating Multilingual Voices Using Speaker Space Translation Based on Bilingual Speaker Data (2020) — 19 citations
- Neural Text-to-Speech Adaptation from Low Quality Public Recordings (2019) — 27 citations
- An investigation of the ‘female camouflage effect’ in autism using a computerized ADOS-2 and a test of sex/gender differences (2016) — 305 citations
- Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition (2013) — 362 citations
- The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism (2013) — 805 citations