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Found 11 papers, 6 papers with code
Pyreal: A Framework for Interpretable ML Explanations
Users in many domains use machine learning (ML) predictions to help them make decisions.
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method.
Sintel: A Machine Learning Framework to Extract Insights from Signals
The detection of anomalies in time series data is a critical task with many monitoring applications.
The Need for Interpretable Features: Motivation and Taxonomy
Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their features.
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks.
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making
Machine learning (ML) is being applied to a diverse and ever-growing set of domains.
Superresolving second-order correlation imaging using synthesized colored noise speckles
We present a novel method to synthesize non-trivial speckles that can enable superresolving second-order correlation imaging.
Optics Image and Video Processing
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018.
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts.
DeepTracker: Visualizing the Training Process of Convolutional Neural Networks
To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log.
