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"Privacy-Preserving federated learning in medical diagnosis with homomo" by Hanchao Ku, Willy Susilo et al.

Unlike traditional centralized machine learning, distributed machine learning provides more efficient and useful application scenarios. However, distributed learning may not meet some security requirements. For example, in medical treatment and diagnosis, an increasing number of people are using IoT devices to record their personal data, when training medical data, the users are not willing to reveal their private data to the training party. How to collect and train the data securely has become the main problem to be resolved. Federated learning can combine a large amount of scattered data for training, and protect user data. Compared with general distributed learning, federated learning is more suitable for training on scattered data. In this paper, we propose a privacy-preserving federated learning scheme that is based on the cryptographic primitive of homomorphic re-encryption, which can protect user data through homomorphic re-encryption and trains user data through batch gradient ....

Federated Learning , Gradient Descent , Omomorphic Re Encryption , Privacy Preserving ,

Word Embeddings

Word Embeddings
lena-voita.github.io - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from lena-voita.github.io Daily Mail and Mail on Sunday newspapers.

Timothy Hospedales , Tomas Mikolov , Jacob Eisenstein , Carl Allen , University Of Edinburgh , Look Up Table , Discrete Symbols , Machine Learning , Co Occurence Counts , Research Thinking , Pointwise Mutual Information , Semantic Analysis , Deep Learning , Gradient Descent , One Word , Parameter Learning , Continuous Bag Of Words , Negative Sampling , Related Papers , Global Vectors , Walk Through Space , Rare Words , Analogies Explained , Towards Understanding Word Embeddings , Best Paper Honourable Mention , Bigger Analogy Test Set ,

Documentation

Ensmallen | flexible C++ library for efficient numerical optimization ....

Ryanr Curtin , Suryoday Basak , Zhihao Lou , Rahul Ganesh Prabhu , Conrad Sanderson , Marcus Edel , Journal Of Machine Learning Research , Machine Learning Research , Augmented Lagrangian , Big Batch , Covariance Matrix Adaptation Evolution Strategy , Neural Evolution , Moving Leader , Incremental Quasi Newton , Gradient Descent , Multi Objective Evolutionary Algorithm , Differential Crossover , Polynomial Mutation , Single Objective , Bayesian Bootstrap , Non Dominated Sorting Genetic Algorithm , Optimistic Adam , Optmistic Mirror Descent , Particles Number , Perturbation Stochastic Approximation , Recursive Gradient Algorithm ,

India's First AI NFT art exhibition goes underway

India's First AI NFT art exhibition goes underway
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New Delhi , Aparajita Jain , Feileacan Mccormick , Evan Arman , Scott Eaton , Harshit Agrawal , Siddhi Jain , David Young , South Asia , Sofia Crespo , Intertwined Intelligences , Nature Morte , Gradient Descent , Artificial Intelligences , Augmented Reality , புதியது டெல்ஹி , அபராஜித ஜெயின் , வேன் ஆர்மன் , ஸ்காட் ஈட்டன் , சித்தி ஜெயின் , டேவிட் இளம் , தெற்கு ஆசியா , சோபியா கிரெஸ்பா , இயற்கை மார்ட் , செயற்கை புத்திசாலித்தனம் , பெரிதாக்கப்பட்டது ரியாலிடீ ,

Incremental Risk Minimization Algorithm

Incremental Risk Minimization Algorithm
Incremental Regression with Polynomials ↑
Incremental (or on-line) learning regression is the process of adapting a model one example at a time without accumulating a batch of data. It has the advantages of allowing continuous adaptation to non-stationary environments, easily handling big data through stream processing, and a fixed low computation and memory demand.
The easiest solution is to perform a gradient descent on a squared error metric with each new training example. But this solution does not work well for complex model structures. Especially, the influence of a non-linear transformation of the inputs through a fixed model structure has long been an open problem. During my PhD I worked on an approach which is able to deal with a broad class of non-linear model structures. Its emphasis is on minimizing the effect of local training examples on changes of the global model. Thus, it yields a robust behavio ....

United States , United Kingdom , Cambridge University , Cambridge University Press , A Library For Analysis Of Online , Journal Of Machine Learning Research , International Conference On Machine , International Conference On , Incremental Risk Minimization Algorithm , Stop Auto Sample , Risk Functional , Gradient Descent , Linear In Parameters , Risk Minimization Algorithm , On Line Machine Learning , Regression Tasks , Minimized Change , Global Mapping , Optimized Local Learning , Incremental Risk , Evolving Systems , Localized On Line Learning , International Conference , Adaptive Intelligent Systems , Online Learning , Minimal Change ,