Ambuj Mehrish

Artificial Intelligence · 2023–2026

A researcher in the Science corpus: 4 papers; trusts 39 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 195 citations into it
• Signal Processing — 144 citations into it
• Computer Vision and Pattern Recognition — 70 citations into it
• Generative Models — 38 citations into it
• Statistical and Nonlinear Physics — 10 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Ambuj Mehrish trusts overall

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

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

Papers from Ambuj Mehrish's perspective

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

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

Their papers

• Constrained Dominant Sets for Multimodal Document Question Answering (2026)
• Min-Cost Flow Routing for Evidence Assembly in Long Multimodal Documents (2026)
• A review of deep learning techniques for speech processing (2023) — 354 citations
• Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model (2023) — 205 citations