Jesse Engel
Computer Vision and Pattern Recognition · 2015–2023
A researcher in the Science corpus: 22 papers; trusts 951 researchers and is trusted by 78 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 336 citations into it; #3,422 of 21,575 by trust here, #2,281 by citations
- Artificial Intelligence — 327 citations into it
- Generative Models — 226 citations into it; #457 of 1,956 by trust here, #418 by citations
- Signal Processing — 199 citations into it; #752 of 7,572 by trust here, #248 by citations
- Model Architectures — 110 citations into it
- Natural Language Processing — 110 citations into it
- Speech & Audio — 102 citations into it; #600 of 1,076 by trust here, #330 by citations
- Optimization & Training — 48 citations into it
Whom Jesse Engel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Kaiming He
- Ilya Sutskever
- Aäron van den Oord
- Alex Graves
- Yann LeCun
- Karen Simonyan
Who trusts Jesse Engel overall
- Adam Roberts
- Yingtao Tian
- Gautam Mittal
- Adam Polyak
- Arash Vahdat
- Lamtharn Hantrakul
- Lior Wolf
- Antoine Caillon
- Curtis Hawthorne
- Chris Donahue
Papers from Jesse Engel's perspective
Ordered by how strongly the researchers Jesse Engel 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders (2017) — 781 citations
- Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset (2018) — 596 citations
- GANSynth: Adversarial Neural Audio Synthesis (2019) — 425 citations
- Symbolic Music Generation with Diffusion Models (2021) — 234 citations
- Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models (2017) — 151 citations
- A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music (2018) — 554 citations
- DDSP: Differentiable Digital Signal Processing (2020) — 489 citations
- MusicLM: Generating Music From Text (2023) — 722 citations
- Onsets and Frames: Dual-Objective Piano Transcription (2017) — 335 citations
- Noise2Music: Text-conditioned Music Generation with Diffusion Models (2023) — 263 citations
- Latent Translation: Crossing Modalities by Bridging Generative Models (2019) — 18 citations
- General-purpose, long-context autoregressive modeling with Perceiver AR (2022) — 87 citations
- Variable-rate discrete representation learning (2021) — 36 citations
- Learning Multiscale Features Directly from Waveforms (2016) — 66 citations
- SingSong: Generating musical accompaniments from singing (2023) — 79 citations
- Encoding Musical Style with Transformer Autoencoders (2019) — 104 citations
- MidiMe: Personalizing a MusicVAE model with user data (2019) — 33 citations
- Fast and Flexible Neural Audio Synthesis (2019) — 35 citations
- Learning via social awareness: improving sketch representations with facial feedback (2018) — 13 citations
- Learning a Latent Space of Multitrack Measures (2018) — 55 citations