Xin Jiang
Artificial Intelligence · with Liu · 2015–2023
A researcher in the Science corpus: 26 papers; trusts 896 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 772 citations into it; #5,172 of 35,345 by trust here, #1,512 by citations
- Natural Language Processing — 427 citations into it; #5,195 of 5,688 by trust here, #2,082 by citations
- Computer Vision and Pattern Recognition — 376 citations into it; #6,223 of 21,575 by trust here, #1,706 by citations
- Large Language Models — 189 citations into it; #128 of 799 by trust here, #180 by citations
- Evaluation & Benchmarks — 136 citations into it
- Efficiency & Compression — 128 citations into it; #291 of 1,264 by trust here, #170 by citations
- Model Architectures — 103 citations into it
- Representation & Self-Supervised Learning — 77 citations into it
Whom Xin Jiang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Oriol Vinyals
- Jimmy Ba
- Alex Graves
Who trusts Xin Jiang overall
- Zi-Mei Yao
- Lu Hou
- Sudha Rao
- Sehoon Kim
- Bhuwan Dhingra
- Wei Zhang
- Xiaoxia Wu
- Ruobing Xie
- Duyu Tang
- Young Jin Kim
Papers from Xin Jiang's perspective
Ordered by how strongly the researchers Xin Jiang 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- FILIP: Fine-grained Interactive Language-Image Pre-Training (2021) — 865 citations
- TinyBERT: Distilling BERT for Natural Language Understanding (2019) — 2,430 citations
- ERNIE: Enhanced Language Representation with Informative Entities (2019) — 1,575 citations
- PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation (2021) — 243 citations
- Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation (2022) — 113 citations
- Generate & Rank: A Multi-task Framework for Math Word Problems (2021) — 159 citations
- DynaBERT: Dynamic BERT with Adaptive Width and Depth (2020) — 390 citations
- Neural Generative Question Answering (2015) — 218 citations
- PanGu-Coder: Program Synthesis with Function-Level Language Modeling (2022) — 87 citations
- Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence (2019) — 30 citations
- Pretrained Language Models for Document-Level Neural Machine Translation (2019) — 21 citations
- BinaryBERT: Pushing the Limit of BERT Quantization (2020) — 276 citations
- bert2BERT: Towards Reusable Pretrained Language Models (2021) — 87 citations
- TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020) — 238 citations
- Aligning Large Language Models with Human: A Survey (2023) — 495 citations
- Read before Generate! Faithful Long Form Question Answering with Machine Reading (2022) — 77 citations
- Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022) — 55 citations
- Compression of Generative Pre-trained Language Models via Quantization (2022) — 119 citations
- Training Multilingual Pre-trained Language Model with Byte-level Subwords (2021) — 24 citations
- CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems (2021) — 46 citations