Hamid Palangi
Artificial Intelligence · with Gao · 2013–2021
A researcher in the Science corpus: 11 papers; trusts 445 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 171 citations into it; #6,537 of 35,345 by trust here, #3,498 by citations
- Natural Language Processing — 115 citations into it; #3,074 of 5,688 by trust here, #1,955 by citations
- Computer Vision and Pattern Recognition — 108 citations into it; #4,835 of 21,575 by trust here, #3,476 by citations
- Model Architectures — 53 citations into it
- Multimodal Learning — 44 citations into it; #399 of 1,211 by trust here, #334 by citations
- Signal Processing — 33 citations into it
- Computational Mechanics — 26 citations into it
- Large Language Models — 25 citations into it
Whom Hamid Palangi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Graves
- Li Deng
- Ilya Sutskever
- Sepp Hochreiter
- J. Urgen Schmidhuber
- Tomas Mikolov
- Matthew D. Zeiler
- Yann LeCun
Who trusts Hamid Palangi overall
- Li Deng
- Kezhen Chen
- Paul Smolensky
- Zhe Gan
- Jianfeng Gao
- Imanol Schlag
- Gautier Izacard
- Xiaowei Hu
- Jianfeng Wang
- Pengchuan Zhang
Papers from Hamid Palangi's perspective
Ordered by how strongly the researchers Hamid Palangi 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Unified Vision-Language Pre-Training for Image Captioning and VQA (2019) — 1,067 citations
- Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval (2015) — 861 citations
- Question-Answering with Grammatically-Interpretable Representations (2017) — 54 citations
- Learning Input and Recurrent Weight Matrices in Echo State Networks (2013) — 19 citations
- HUBERT Untangles BERT to Improve Transfer across NLP Tasks (2019) — 19 citations
- NICE: Neural Image Commenting with Empathy (2021) — 9 citations
- Natural- to formal-language generation using Tensor Product Representations (2019) — 3 citations
- Mapping natural-language problems to formal-language solutions using structured neural representations (2019) — 28 citations
- Deep Learning of Grammatically-Interpretable Representations Through Question-Answering (2017) — 18 citations
- Distributed Compressive Sensing: A Deep Learning Approach (2015) — 118 citations
- Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors (2013) — 16 citations