Service-as-Agentic AI

Resources

Following are some of the publicly available, top AI research papers:

  1. “Denoising Diffusion Probabilistic Models” (2020) by Ho et al. Link: Denoising Diffusion Probabilistic Models
  2. “Scaling Laws for Neural Language Models” (2020) by Kaplan et al. Link: Scaling Laws for Neural Language Models
  3. “GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism” (2019) by Huang et al. Link: GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
  4. “The Annotated Transformer” (2018) Link: The Annotated Transformer
  5. “Relational recurrent neural networks” (2018) by Santoro et al. Link: Relational recurrent neural networks
  6. “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” (2018) by Devlin et al. Link: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  7. “BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis” (2018) by Brock et al. Link: Large Scale GAN Training for High Fidelity Natural Image Synthesis
  8. “StyleGAN: A Style-Based Generator Architecture for Generative Adversarial Networks” (2018) by Karras et al. Link: A Style-Based Generator Architecture for Generative Adversarial Networks
  9. “Glow: Generative Flow with Invertible 1×1 Convolutions” (2018) by Kingma and Dhariwal. Link: Glow: Generative Flow with Invertible 1×1 Convolutions
  10. “Attention Is All You Need”(2017) by Vaswani et al. Link: Attention Is All You Need
  11. “A Simple Neural Network Module for Relational Reasoning” (2017) by Santoro et al. Link: A Simple Neural Network Module for Relational Reasoning
  12. “Neural Discrete Representation Learning” (2017) by van den Oord et al. Link: Neural Discrete Representation Learning
  13. “Neural Discrete Representation Learning” (2017) by van den Oord et al. Link: Neural Discrete Representation Learning
  14. “Variational Lossy Autoencoder” (2017) by Kingma et al. Link: Variational Lossy Autoencoder
  15. “ImageNet Classification with Deep Convolutional Neural Networks” (2017) by Sutskever et al. Link: ImageNet Classification with Deep Convolutional Neural Networks
  16. “Pointer Networks” (2017) by Vinyals et al. Link: Pointer Networks
  17. “Improved Techniques for Training GANs” (2016) by Salimans et al. Link: Improved Techniques for Training GANs
  18. “Pixel Recurrent Neural Networks” (2016) by Oord et al. Link: Pixel Recurrent Neural Networks
  19. “Identity Mappings in Deep Residual Networks” (2016) by Zhang et al. Link: Identity Mappings in Deep Residual Networks
  20. “Neural Machine Translation by Jointly Learning to Align and Translate” (2016) by Bahdanau et al. Link: Neural Machine Translation by Jointly Learning to Align and Translate
  21. “Order Matters: Sequence To Sequence For Sets” (2016) by Vinyals et al. Link: Order Matters: Sequence To Sequence For Sets
  22. “Multi-Scale Context Aggregation by Dilated Convolutions” (2016) by Yu and Koltun. Link: Multi-Scale Context Aggregation by Dilated Convolutions
  23. “Recurrent Neural Network Regularization” (2015) by Sutskever et al. Link: Recurrent Neural Network Regularization
  24. “The Unreasonable Effectiveness of Recurrent Neural Networks” (2015) by Andrej Karpathy. Link: The Unreasonable Effectiveness of Recurrent Neural Networks
  25. “Understanding LSTM Networks” (2015) by Christopher Olah. Link: Understanding LSTM Networks
  26. “Deep Residual Learning for Image Recognition” (2015) by Zhang et al. Link: Deep Residual Learning for Image Recognition
  27. “Generative Adversarial Networks” (2014) by Ian Goodfellow et al. Link: Generative Adversarial Networks
  28. “Neural Turing Machines” (2014) by Graves et al. Link: Neural Turing Machines
  29. “Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton” (2014) by Aaronson et al. Link: Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
  30. “Auto-Encoding Variational Bayes” (2013) by Kingma and Welling. Link: Auto-Encoding Variational Bayes
  31. “Keeping Neural Networks Simple by Minimizing the Description Length of the Weights” by Geoffrey E. Hinton and Drew van Camp. Link: Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
  32. “A Tutorial Introduction to the Minimum Description Length Principle” (2004) by Peter Grunwald. Link: A Tutorial Introduction to the Minimum Description Length Principle