Rithesh Kumar
Artificial Intelligence · 2016–2023
A researcher in the Science corpus: 6 papers; trusts 290 researchers and is trusted by 48 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 107 citations into it; #10,296 of 35,345 by trust here, #4,888 by citations
- Computer Vision and Pattern Recognition — 99 citations into it; #5,999 of 21,575 by trust here, #3,489 by citations
- Generative Models — 97 citations into it; #395 of 1,956 by trust here, #252 by citations
- Signal Processing — 82 citations into it; #903 of 7,572 by trust here, #227 by citations
- Speech & Audio — 51 citations into it
- Model Architectures — 23 citations into it
- Optimization & Training — 21 citations into it
- Probabilistic & Bayesian Methods — 20 citations into it
Whom Rithesh Kumar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Aäron van den Oord
- Yann LeCun
- Alex Krizhevsky
- Kaiming He
- Ilya Sutskever
- Alex Graves
- Oriol Vinyals
Who trusts Rithesh Kumar overall
- Zengyi Li
- Yilun Du
- Hubert Siuzdak
- Xu Tan
- Erik Nijkamp
- Chenpeng Du
- Wei Ping
- Yang Song
- Yubei Chen
- Ruiqi Gao
Papers from Rithesh Kumar's perspective
Ordered by how strongly the researchers Rithesh Kumar 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)
- GENERATIVE ADVERSARIAL NETS
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- SampleRNN: An Unconditional End-to-End Neural Audio Generation Model (2016) — 630 citations
- Maximum Entropy Generators for Energy-Based Models (2019) — 119 citations
- MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis (2019) — 1,145 citations
- High-Fidelity Audio Compression with Improved RVQGAN (2023) — 803 citations
- Chunked Autoregressive GAN for Conditional Waveform Synthesis (2021) — 96 citations
- NU-GAN: High resolution neural upsampling with GAN (2020) — 35 citations