Jyoti Aneja
Artificial Intelligence · 2017–2024
A researcher in the Science corpus: 8 papers; trusts 756 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 238 citations into it; #15,940 of 35,345 by trust here, #7,797 by citations
- Computer Vision and Pattern Recognition — 220 citations into it; #9,440 of 21,575 by trust here, #11,083 by citations
- Natural Language Processing — 74 citations into it
- Evaluation & Benchmarks — 64 citations into it; #2,751 of 3,147 by trust here, #2,590 by citations
- Multimodal Learning — 59 citations into it; #515 of 1,211 by trust here, #758 by citations
- Generative Models — 53 citations into it
- Representation & Self-Supervised Learning — 32 citations into it
- Model Architectures — 26 citations into it
Whom Jyoti Aneja trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Kaiming He
- Karen Simonyan
- Yann LeCun
- Ilya Sutskever
- Ross B. Girshick
- Aäron van den Oord
Who trusts Jyoti Aneja overall
- Marcella Cornia
- Matteo Stefanini
- Mohammed Bany Muhammad
- Idan Schwartz
- Arash Vahdat
- Lorenzo Baraldi
- Abhishek Sinha
- Yoad Tewel
- Byeongchang Kim
- Guang Li
Papers from Jyoti Aneja's perspective
Ordered by how strongly the researchers Jyoti Aneja 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Convolutional Image Captioning (2017) — 401 citations
- Textbooks Are All You Need (2023) — 626 citations
- Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning (2019) — 72 citations
- Phi-4 Technical Report (2024) — 716 citations
- Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024) — 2,340 citations
- Explaining Patterns in Data with Language Models via Interpretable Autoprompting (2022) — 58 citations
- ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models (2022) — 195 citations
- NCP-VAE: Variational Autoencoders with Noise Contrastive Priors (2020) — 37 citations