Bajibabu Bollepalli
Artificial Intelligence · 2017–2022
A researcher in the Science corpus: 6 papers; trusts 90 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 96 citations into it; #8,505 of 35,345 by trust here, #13,163 by citations
- Speech & Audio — 63 citations into it
- Signal Processing — 60 citations into it; #1,413 of 7,572 by trust here, #1,904 by citations
- Natural Language Processing — 42 citations into it; #3,545 of 5,688 by trust here, #4,682 by citations
- Generative Models — 31 citations into it
- Computer Vision and Pattern Recognition — 28 citations into it
- Optimization & Training — 10 citations into it
- Model Architectures — 9 citations into it
Whom Bajibabu Bollepalli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Heiga Zen
- Hideki Kawahara
- Aäron van den Oord
- Lauri Juvela
- Geoffrey E. Hinton
- Sergey Ioffe
- Jimmy Ba
- Individualized Treat
- Yoshua Bengio
Who trusts Bajibabu Bollepalli overall
- Lauri Juvela
- Ryuichi Yamamoto
- Eunwoo Song
- Yuki Saito
- Yang Cui
- Da-Rong Liu
- Jae-Min Kim
- Vassilis Tsiaras
- Paavo Alku
- Yang Ai
Papers from Bajibabu Bollepalli's perspective
Ordered by how strongly the researchers Bajibabu Bollepalli 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- GENERATIVE ADVERSARIAL NETS
Their papers
- GlotNet—A Raw Waveform Model for the Glottal Excitation in Statistical Parametric Speech Synthesis (2019) — 44 citations
- Distribution Augmentation for Low-Resource Expressive Text-To-Speech (2022) — 10 citations
- GELP: GAN-Excited Liner Prediction for Speech Synthesis from Mel-spectrogram (2019) — 18 citations
- GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram (2019) — 26 citations
- Speaker-independent raw waveform model for glottal excitation (2018) — 39 citations
- Generative Adversarial Network-Based Glottal Waveform Model for Statistical Parametric Speech Synthesis (2017) — 50 citations