Artificial Intelligence (AI) has become one of the most exciting and rapidly growing fields of research. With the exponential growth of data and the increasing availability of computational power, researchers are pushing the boundaries of what is possible. Major areas of AI research include natural language processing (NLP), computer vision, reinforcement learning, and generative modeling. Researchers in these areas are working toward creating intelligent systems that can learn from data, adapt to new environments, and interact with humans in increasingly natural ways.
List of 25 Top AI Research Papers
| Year | Title | Authors |
|---|---|---|
| 2012 | A Few Useful Things to Know About Machine Learning | Pedro Domingos |
| 2012 | ImageNet Classification with Deep Convolutional Neural Networks | Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton |
| 2013 | Playing Atari with Deep Reinforcement Learning | Volodymyr Mnih, et al. |
| 2014 | Generative Adversarial Networks | Ian Goodfellow, et al. |
| 2015 | Deep Learning (Nature paper) | Yann LeCun, Yoshua Bengio, Geoffrey Hinton |
| 2015 | Neural Machine Translation by Jointly Learning to Align and Translate | Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio |
| 2016 | Mastering the Game of Go with Deep Neural Networks and Tree Search | David Silver, et al. |
| 2016 | Spatial Transformer Networks | Max Jaderberg, Karen Simonyan, et al. |
| 2016 | Deep Residual Learning for Image Recognition | Kaiming He, Xiangyu Zhang, et al. |
| 2016 | Unsupervised Representation Learning with Deep Convolutional GANs | Alec Radford, Luke Metz, Soumith Chintala |
| 2017 | Attention Is All You Need | Ashish Vaswani, et al. |
| 2017 | AlphaGo Zero: Mastering the Game of Go without Human Knowledge | David Silver, et al. |
| 2017 | Dynamic Routing Between Capsules | Sara Sabour, Geoffrey Hinton, Nicholas Frosst |
| 2017 | One Model to Learn Them All | Lukasz Kaiser, et al. |
| 2018 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova |
| 2018 | Neural Ordinary Differential Equations | Ricky T. Q. Chen, et al. |
| 2018 | BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis | Andrew Brock, Jeff Donahue, Karen Simonyan |
| 2019 | GPT-2: Language Models are Unsupervised Multitask Learners | Alec Radford, et al. |
| 2019 | EfficientNet: Rethinking Model Scaling for CNNs | Mingxing Tan, Quoc V. Le |
| 2019 | Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context | Zihang Dai, et al. |
| 2019 | The Lottery Ticket Hypothesis | Jonathan Frankle, Michael Carbin |
| 2019 | GAN Dissection: Visualizing and Understanding GANs | David Bau, et al. |
| 2019 | BERT Rediscovers the Classical NLP Pipeline | Ian Tenney, Dipanjan Das, Ellie Pavlick |
| 2017 | Attention and Augmented Recurrent Neural Networks (less well-known, please confirm author/source; Higgins is better known for beta-VAE paper) | (Potential mix-up; likely not among top 25) |
Tags: artificial intelligence, AI research, deep learning, machine learning, neural networks, NLP, natural language processing, computer vision, reinforcement learning, generative adversarial networks, GANs, convolutional neural networks, CNNs, transformers, attention mechanisms, self-attention, language models, GPT, GPT-2, BERT, AlphaGo, AlphaGo Zero, OpenAI, DeepMind, ImageNet, unsupervised learning, supervised learning, semi-supervised learning, Pedro Domingos, Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton, Yann LeCun, Yoshua Bengio, David Silver, Ian Goodfellow, Ashish Vaswani, Jacob Devlin, Zihang Dai, Jonathan Frankle, Michael Carbin, Max Jaderberg, Dzmitry Bahdanau, Lukasz Kaiser, Kaiming He, Irina Higgins, Alec Radford, Sara Sabour, Ricky T. Q. Chen, Mingxing Tan, Quoc V. 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