mmalekzadeh / replacement-autoencoderLinks
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis (IoTDI'18)
☆18Updated 3 years ago
Alternatives and similar repositories for replacement-autoencoder
Users that are interested in replacement-autoencoder are comparing it to the libraries listed below
Sorting:
- Code for data processing used for the experiment in paper "Deepsense: a unified deep learning framework for time-series mobile sensing da…☆29Updated 7 years ago
- Code for the PIDForest algorithm for anomaly detection☆27Updated 5 years ago
- Adversarial Attacks on Deep Neural Networks for Time Series Classification☆77Updated 4 years ago
- neural style tranfer from one walking/gait cycle to another; ArXiv:1606.03238 & ArXiv:1508.06576☆13Updated 7 years ago
- Anomaly detection on time series using Deep Learning techniques☆29Updated 5 years ago
- Use CNN to classify time series data for activity recognition☆26Updated 9 years ago
- Code repository for experiments on deep architecture for HAR in ubicomp☆23Updated 8 years ago
- Online multiclass boosting algorithm that uses VFDT as weak learners☆17Updated 6 years ago
- [ti]ny [li]ttle machine learning [tool]box - Machine learning, anomaly detection, one-class classification, and structured output predict…☆44Updated 6 years ago
- Example code for neural-network-based anomaly detection of time-series data (uses LSTM)☆181Updated 8 years ago
- Deepsense: a unified deep learning framework for time-series mobile sensing data processing.☆198Updated 5 years ago
- Files submitted to kdd2018 for EFDT paper☆22Updated 6 years ago
- Sequence-to-sequence autoencoder for unsupervised learning of nonlinear dynamics (Tensorflow).☆30Updated 3 years ago
- Static (Python) and streaming (C++) implementations of xStream (KDD 2018).☆30Updated 7 years ago
- Experiments on unsupervised anomaly detection using variational autoencoder. The variational autoencoder is implemented in Pytorch.☆67Updated last year
- Code for 'Vulnerability of deep learning based gait biometric recognition to adversarial perturbations'