Xisen Jin

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 13 papers; trusts 908 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 418 citations into it; #17,528 of 35,345 by trust here, #6,956 by citations
• Natural Language Processing — 212 citations into it; #4,465 of 5,688 by trust here, #3,369 by citations
• Computer Vision and Pattern Recognition — 178 citations into it; #18,274 of 21,575 by trust here, #9,187 by citations
• Representation & Self-Supervised Learning — 80 citations into it
• Meta & Continual Learning — 74 citations into it; #914 of 1,041 by trust here, #559 by citations
• Safety, Robustness & Interpretability — 72 citations into it
• Evaluation & Benchmarks — 51 citations into it
• Large Language Models — 39 citations into it

Whom Xisen Jin trusts overall

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

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

Who trusts Xisen Jin overall

1. Michael Sejr Schlichtkrull
2. Nicola De Cao
3. Tanmay Garg
4. Brendan Kennedy
5. Dheeraj Rajagopal
6. Huihan Yao
7. Karolina Stańczak
8. Fei Ye
9. Ivan Titov
10. Sarah Masud

Papers from Xisen Jin's perspective

Ordered by how strongly the researchers Xisen Jin 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Visually Grounded Continual Learning of Compositional Phrases (2020) — 18 citations
• Refining Language Models with Compositional Explanations (2021) — 38 citations
• Gradient-based Editing of Memory Examples for Online Task-free Continual Learning (2020) — 117 citations
• Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures (2018) — 349 citations
• On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning (2021) — 84 citations
• Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning (2021) — 47 citations
• Dataless Knowledge Fusion by Merging Weights of Language Models (2022) — 406 citations
• Contextualizing Hate Speech Classifiers with Post-hoc Explanation (2020) — 158 citations
• Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2021) — 153 citations
• Visually Grounded Continual Learning of Compositional Semantics (2020) — 4 citations
• Gradient Based Memory Editing for Task-Free Continual Learning (2020) — 31 citations
• TOWARDS HIERARCHICAL IMPORTANCE ATTRIBU- (2019)
• Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs (2019) — 493 citations