Kartik Audhkhasi

Artificial Intelligence · 2015–2025

A researcher in the Science corpus: 12 papers; trusts 333 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it; #16,630 of 35,345 by trust here, #8,007 by citations
• Signal Processing — 135 citations into it; #4,750 of 7,572 by trust here, #1,504 by citations
• Computer Vision and Pattern Recognition — 110 citations into it
• Speech & Audio — 89 citations into it; #800 of 1,076 by trust here, #405 by citations
• Natural Language Processing — 77 citations into it
• Multimodal Learning — 52 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Model Architectures — 38 citations into it

Whom Kartik Audhkhasi trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. J. Urgen Schmidhuber
6. Alex Krizhevsky
7. Yann LeCun
8. Ilya Sutskever
9. Sepp Hochreiter
10. Kaiming He

Who trusts Kartik Audhkhasi overall

1. Jinyu Li
2. Dong Yu
3. Albert Zeyer
4. Gakuto Kurata
5. Zhong Meng
6. Yonatan Belinkov
7. Suyoun Kim
8. Shane Settle
9. Shubham Toshniwal
10. Loren Lugosch

Papers from Kartik Audhkhasi's perspective

Ordered by how strongly the researchers Kartik Audhkhasi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• AVLnet: Learning Audio-Visual Language Representations from Instructional Videos (2020) — 148 citations
• English Conversational Telephone Speech Recognition by Humans and Machines (2017) — 374 citations
• Direct Acoustics-to-Word Models for English Conversational Speech Recognition (2017) — 71 citations
• Acoustically Grounded Word Embeddings for Improved Acoustics-to-word Speech Recognition (2019) — 35 citations
• Multilingual representations for low resource speech recognition and keyword search (2015) — 103 citations
• Knowledge distillation across ensembles of multilingual models for low-resource languages (2017) — 61 citations
• Advancing Sequence-to-Sequence Based Speech Recognition (2019) — 25 citations
• Improved Knowledge Distillation from Bi-Directional to Uni-Directional LSTM CTC for End-to-End Speech Recognition (2018) — 44 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Grounding Spoken Words in Unlabeled Video (2019) — 24 citations
• Building Competitive Direct Acoustics-to-Word Models for English Conversational Speech Recognition (2017) — 154 citations
• Invariant Representations for Noisy Speech Recognition (2016) — 70 citations