In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpretable and transferable learning is considered for studying and optimizing the trade-offs between the privacy, interpretability, and transferability aspects of trustworthy AI. A variational membership-mapping Bayesian model is used for the analytical approximation of the defined information theoretic measures for privacy leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures by maximizing a lower-bound using variational optimization. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress in individuals using heart rate variability analysis.
Marvin is a deep learning framework designed first and foremost to be hackable. It is naively simple for fast prototyping, uses only basic C/C++, and only calls CUDA and cuDNN as dependencies.
Marvin is a deep learning framework designed first and foremost to be hackable. It is naively simple for fast prototyping, uses only basic C/C++, and only calls CUDA and cuDNN as dependencies.
Algorithmic music composition has developed a lot in the last few years, but the idea has a long history. In some sense, the first automatic music came from nature: Chinese windchimes, ancient Greek…
A collection of code samples, recipes and tutorials on the various ways you can use the Congitive Toolkit against scenarios for image, text and speech data.
Part I: Intuition (you are reading it now) Part II: How Capsules Work Part III: Dynamic Routing Between Capsules Part IV: CapsNet Architecture (coming soon) Quick announcement about our new…
In the second part of our "A Mathless Guide to Neural Networks," we’ll take a look at why high-quality, labeled data is so important, where it comes from,..
You’ve framed your problem, prepared your datasets, designed your models and revved up your GPUs. With bated breath, you start training your neural network, hoping to return in a few days to great…
Facebook and Microsoft are today introducing Open Neural Network Exchange (ONNX) format, a standard for representing deep learning models that enables models to be transferred between frameworks. ONNX is the first step toward an open ecosystem where AI developers can easily move between state-of-the-art tools and choose the combination that is best for them. When…
Jeremy is talking about that CNN maybe will take over by the end of the year. What would be the best solution for a time series with parallel parameters that normally use LSTM/GRU to solve before? For example predicting…
SIMBRAIN is a free tool for building, running, and analyzing neural-networks (computer simulations of brain circuitry). Simbrain aims to be as visual and easy-to-use as possible. See our design goals. Unique features of Simbrain include its integrated "world components" and its ability to represent a network's state space. Simbrain is written in Java and runs on Windows, Mac OS X, and Linux. Click here for a video introduction to some of Simbrain's features, check out the screenshots, or just download the software and start experimenting. Simbrain is open source, and constantly evolving. We'd love for you to join our team. To discuss any aspect of Simbrain check out the forum.
Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the LGPL license, and it's FREE for you to use it.
"NuPIC implements a hierarchical temporal memory system (HTM) patterned after the human neocortex. We expect NuPIC to be used on problems that, generally speaking, involve identifying patterns in complex data."