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5 papers
2017Advances in Neural Information Processing Systems (NeurIPS) 2017, pp. 5998-6008, arXiv:1706.03762
Attention Is All You Need
Finding: The Transformer achieved state-of-the-art translation quality while being significantly faster to train due to parallelization.
Transformerattention
2016IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (DOI: 10.1109/CVPR.2016.91)
You Only Look Once: Unified, Real-Time Object Detection
Finding: YOLO achieved state-of-the-art performance at incredibly high speeds (up to 45 frames per second on standard hardware, and 155 fps for smaller versions), proving that a single end-to-end network could achieve both high accuracy and real-time processing.
object detectionYOLO
2012Advances in Neural Information Processing Systems (NeurIPS) 2012, pp. 1097-1105
ImageNet Classification with Deep Convolutional Neural Networks
Finding: AlexNet won the ImageNet 2012 competition by a large margin (15.3% top-5 error vs 26.2% for the second best), proving that deep CNNs are highly effective on large-scale data.
CNNImageNet
1997Neural Computation, vol. 9, no. 8, pp. 1735-1780, DOI: 10.1162/neco.1997.9.8.1735
Long Short-Term Memory
Finding: LSTM outperformed vanilla RNNs on tasks like speech recognition, machine translation, and text generation, successfully modeling long-term dependencies.
RNNLSTM
1986Nature, vol. 323, no. 6088, pp. 533-536, DOI: 10.1038/323533a0
Learning representations by back-propagating errors
Finding: Backpropagation effectively trains multi-layer networks for nonlinear tasks like pattern recognition and representation learning.
neural networksbackpropagation