Search Results for author:
Found 6 papers, 0 papers with code
Efficient Encrypted Inference on Ensembles of Decision Trees
In this work, we propose a framework to transfer knowledge extracted by complex decision tree ensembles to shallow neural networks (referred to as DTNets) that are highly conducive to encrypted inference.
Efficient CNN Building Blocks for Encrypted Data
Using the CKKS scheme available in the open-source HElib library, we show that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters have a major impact on the design of the machine learning model (especially, the choice of the activation function and pooling method).
P2L: Predicting Transfer Learning for Images and Semantic Relations
We use this measure, which we call "Predict To Learn" ("P2L"), in the two very different domains of images and semantic relations, where it predicts, from a set of "source" models, the one model most likely to produce effective transfer for training a given "target" model.
Distributed Bundle Adjustment
The runtime of our implementation scales linearly with the number of observed points.
Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images
Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70. 5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%).
Temporal Sequence Modeling for Video Event Detection
Based on this idea, we represent a video by a sequence of visual words learnt from the video, and apply the Sequence Memoizer [21] to capture long-range dependencies in a temporal context in the visual sequence.
