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Main README reflects SeeDot
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README.md
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@ -16,6 +16,7 @@ This repository contains algorithms that shine in this setting in terms of both
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- **ProtoNN**: **Proto**type based k-nearest neighbors (k**NN**) classifier.
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- **ProtoNN**: **Proto**type based k-nearest neighbors (k**NN**) classifier.
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- **EMI-RNN**: Training routine to recover the critical signature from time series data for faster and accurate RNN predictions.
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- **EMI-RNN**: Training routine to recover the critical signature from time series data for faster and accurate RNN predictions.
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- **FastRNN & FastGRNN - FastCells**: **F**ast, **A**ccurate, **S**table and **T**iny (**G**ated) RNN cells.
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- **FastRNN & FastGRNN - FastCells**: **F**ast, **A**ccurate, **S**table and **T**iny (**G**ated) RNN cells.
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- **SeeDot**: Floating-point to fixed-point quantization tool.
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These algorithms can train models for classical supervised learning problems
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These algorithms can train models for classical supervised learning problems
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with memory requirements that are orders of magnitude lower than other modern
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with memory requirements that are orders of magnitude lower than other modern
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@ -27,14 +28,16 @@ The `tf` directory contains code, examples and scripts for all these algorithms
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in TensorFlow. The `cpp` directory has training and inference code for Bonsai and
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in TensorFlow. The `cpp` directory has training and inference code for Bonsai and
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ProtoNN algorithms in C++. Please see install/run instruction in the Readme
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ProtoNN algorithms in C++. Please see install/run instruction in the Readme
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pages within these directories. The `applications` directory has code/demonstrations
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pages within these directories. The `applications` directory has code/demonstrations
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of applications of the EdgeML algorithms.
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of applications of the EdgeML algorithms. The `Tools/SeeDot` directory has the
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quantization tool to generate fixed-point inference code.
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For details, please see our [wiki
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For details, please see our [wiki
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page](https://github.com/Microsoft/EdgeML/wiki/) and our ICML'17 publications
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page](https://github.com/Microsoft/EdgeML/wiki/) and our ICML'17 publications
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on [Bonsai](docs/publications/Bonsai.pdf) and
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on [Bonsai](docs/publications/Bonsai.pdf) and
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[ProtoNN](docs/publications/ProtoNN.pdf) algorithms, NIPS'18 publications on
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[ProtoNN](docs/publications/ProtoNN.pdf) algorithms, NeurIPS'18 publications on
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[EMI-RNN](docs/publications/emi-rnn-nips18.pdf) and
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[EMI-RNN](docs/publications/emi-rnn-nips18.pdf) and
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[FastGRNN](docs/publications/FastGRNN.pdf).
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[FastGRNN](docs/publications/FastGRNN.pdf), PLDI'19 publication on
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[SeeDot](docs/publications/SeeDot.pdf).
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Core Contributors:
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Core Contributors:
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@ -44,6 +47,7 @@ Core Contributors:
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- [Don Dennis](https://dkdennis.xyz)
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- [Don Dennis](https://dkdennis.xyz)
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- [Harsha Vardhan Simhadri](http://harsha-simhadri.org)
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- [Harsha Vardhan Simhadri](http://harsha-simhadri.org)
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- [Shishir Patil](https://shishirpatil.github.io/)
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- [Shishir Patil](https://shishirpatil.github.io/)
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- [Sridhar Gopinath](http://www.sridhargopinath.in/)
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We welcome contributions, comments, and criticism. For questions, please [email
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We welcome contributions, comments, and criticism. For questions, please [email
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Harsha](mailto:harshasi@microsoft.com).
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Harsha](mailto:harshasi@microsoft.com).
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