Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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A u-shaped fully-convolutional encoder-decoder with skip connections trained with elastic-deformation augmentation produces accurate biomedical image segmentations from very small training sets.
TextGrad performs automatic differentiation for compound AI systems by backpropagating natural-language feedback from LLMs to optimize variables ranging from code to molecular structures.
Mixed precision training uses FP16 for most computations, FP32 master weights for accumulation, and loss scaling to enable accurate training of large DNNs with halved memory usage.
MobileNets introduce depthwise separable convolutions plus width and resolution multipliers to produce efficient CNNs that trade off latency and accuracy for mobile and embedded vision applications.
A pruning-quantization-Huffman pipeline compresses deep neural networks 35-49x without accuracy loss.
citing papers explorer
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Deep Residual Learning for Image Recognition
Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
A u-shaped fully-convolutional encoder-decoder with skip connections trained with elastic-deformation augmentation produces accurate biomedical image segmentations from very small training sets.
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TextGrad: Automatic "Differentiation" via Text
TextGrad performs automatic differentiation for compound AI systems by backpropagating natural-language feedback from LLMs to optimize variables ranging from code to molecular structures.
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Mixed Precision Training
Mixed precision training uses FP16 for most computations, FP32 master weights for accumulation, and loss scaling to enable accurate training of large DNNs with halved memory usage.
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNets introduce depthwise separable convolutions plus width and resolution multipliers to produce efficient CNNs that trade off latency and accuracy for mobile and embedded vision applications.
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Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
A pruning-quantization-Huffman pipeline compresses deep neural networks 35-49x without accuracy loss.