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$ python script.py ps
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:200] Initialize GrpcChannelCache for job ps -> {0 -> localhost:9000}
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:200] Initialize GrpcChannelCache
Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization Francis Bach https://papers.nips.cc/paper/7286-efficient-algorithms-for-non-convex-isotonic-regression-through-submodular-optimization
Structure-Aware Convolutional Neural Networks Jianlong Chang https://papers.nips.cc/paper/7287-structure-aware-convolutional-neural-networks
Kalman Normalization: Normalizing Internal Representations Across Network Layers Guangrun Wang https://papers.nips.cc/paper/7288-kalman-normalization-normalizing-internal-representations-across-network-layers
HOGWILD!-Gibbs can be PanAccurate Constantinos Daskalakis https://papers.nips.cc/paper/7289-hogwild-gibbs-can-be-panaccurate
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language Seonghyeon Nam https://papers.nips.cc/paper/7290-text-adaptive-generative-adversarial-networks-manipulating-images-with-natural-language
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis Huaibo Huang https
# Now available here: https://github.com/y0ast/pytorch-snippets/tree/main/minimal_cifar