Wai Lam

Artificial Intelligence · 1993–2023

A researcher in the Science corpus: 16 papers; trusts 563 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 423 citations into it; #19,207 of 35,345 by trust here, #8,439 by citations
• Natural Language Processing — 250 citations into it
• Computer Vision and Pattern Recognition — 122 citations into it
• Information Systems — 73 citations into it; #8,545 of 8,771 by trust here, #2,471 by citations
• Large Language Models — 61 citations into it
• Model Architectures — 46 citations into it
• Representation & Self-Supervised Learning — 42 citations into it
• Evaluation & Benchmarks — 35 citations into it

Whom Wai Lam trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Kyunghyun Cho
7. Kaiming He
8. Dzmitry Bahdanau
9. Yann LeCun
10. Tomas Mikolov

Who trusts Wai Lam overall

1. Rónán Daly
2. Yoshua Bengio
3. Wenxuan Zhang
4. Qiang Shen
5. Xin Li
6. Tristan Deleu
7. Stuart Aitken
8. Lidong Bing
9. David Maxwell Chickering

Papers from Wai Lam's perspective

Ordered by how strongly the researchers Wai Lam 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Exploiting Reasoning Chains for Multi-hop Science Question Answering (2021) — 32 citations
• Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder (2023) — 68 citations
• On the Effectiveness of Parameter-Efficient Fine-Tuning (2022) — 249 citations
• A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges (2022) — 425 citations
• Aspect Sentiment Quad Prediction as Paraphrase Generation (2021) — 298 citations
• Review-based Question Generation with Adaptive Instance Transfer and Augmentation (2019) — 20 citations
• Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (2019) — 322 citations
• Transformation Networks for Target-Oriented Sentiment Classification (2018) — 474 citations
• Product Question Intent Detection using Indicative Clause Attention and Adversarial Learning (2018) — 8 citations
• Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (2018) — 97 citations
• A Unified Model for Opinion Target Extraction and Target Sentiment Prediction (2018) — 354 citations
• Neural Rating Regression with Abstractive Tips Generation for Recommendation (2017) — 338 citations
• Evaluation of Text Summarization in a Cross-lingual Information Retrieval Framework (2011) — 17 citations
• Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007) — 359 citations
• MEAD - A Platform for Multidocument Multilingual Text Summarization (2004) — 406 citations
• Using Causal Information and Local Measures to Learn Bayesian Networks (1993)