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Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors
Similarity search in high-dimensional spaces is an important task for ma...
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Distributed Tera-Scale Similarity Search with MPI: Provably Efficient Similarity Search over billions without a Single Distance Computation
We present SLASH (Sketched LocAlity Sensitive Hashing), an MPI (Message ...
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Experimental Analysis of Locality Sensitive Hashing Techniques for High-Dimensional Approximate Nearest Neighbor Searches
Finding nearest neighbors in high-dimensional spaces is a fundamental op...
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Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
Designing deep learning-based solutions is becoming a race for training ...
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Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing
Supervised learning models are typically trained on a single dataset and...
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TensorFlow-Serving: Flexible, High-Performance ML Serving
We describe TensorFlow-Serving, a system to serve machine learning model...
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FlexServe: Deployment of PyTorch Models as Flexible REST Endpoints
The integration of artificial intelligence capabilities into modern soft...
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It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing
In recent, deep learning has become the most popular direction in machine learning and artificial intelligence. However, preparation of training data is often a bottleneck in the lifecycle of deploying a deep learning model for production or research. Reusing models for inferencing a dataset can greatly save the human costs required for training data creation. Although there exist a number of model sharing platform such as TensorFlow Hub, PyTorch Hub, DLHub, most of these systems require model uploaders to manually specify the details of each model and model downloaders to screen keyword search results for selecting a model. They are in lack of an automatic model searching tool. This paper proposes an end-to-end process of searching related models for serving based on the similarity of the target dataset and the training datasets of the available models. While there exist many similarity measurements, we study how to efficiently apply these metrics without pair-wise comparison and compare the effectiveness of these metrics. We find that our proposed adaptivity measurement which is based on Jensen-Shannon (JS) divergence, is an effective measurement, and its computation can be significantly accelerated by using the technique of locality sensitive hashing.
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