Saksham Singhal

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 10 papers; trusts 350 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 382 citations into it; #22,546 of 35,345 by trust here, #4,602 by citations
• Computer Vision and Pattern Recognition — 273 citations into it
• Natural Language Processing — 188 citations into it; #4,916 of 5,688 by trust here, #2,529 by citations
• Evaluation & Benchmarks — 115 citations into it
• Large Language Models — 104 citations into it; #767 of 799 by trust here, #562 by citations
• Multimodal Learning — 102 citations into it
• Representation & Self-Supervised Learning — 52 citations into it
• Optimization & Training — 15 citations into it

Whom Saksham Singhal 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 Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Jimmy Ba
8. Yann LeCun
9. Jacob Devlin
10. Alexis Conneau

Who trusts Saksham Singhal overall

1. Zewen Chi

Papers from Saksham Singhal's perspective

Ordered by how strongly the researchers Saksham Singhal 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks (2022) — 724 citations
• Language Is Not All You Need: Aligning Perception with Language Models (2023) — 786 citations
• On the Representation Collapse of Sparse Mixture of Experts (2022) — 199 citations
• Foundation Transformers (2022) — 30 citations
• DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders (2021) — 92 citations
• Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs (2025) — 509 citations
• Image as a Foreign Language: BEIT Pretraining for Vision and Vision-Language Tasks (2023) — 533 citations
• mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs (2021) — 82 citations
• XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2021) — 139 citations
• InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (2020) — 442 citations