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Found 14 papers, 6 papers with code
Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes
We theoretically prove the convergence of Shatter and provide a formal analysis demonstrating how Shatter reduces the efficacy of attacks compared to when exchanging full models between participating nodes.
Low-Cost Privacy-Aware Decentralized Learning
This paper introduces ZIP-DL, a novel privacy-aware decentralized learning (DL) algorithm that relies on adding correlated noise to each model update during the model training process.
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches.
Get More for Less in Decentralized Learning Systems
Decentralized learning (DL) systems have been gaining popularity because they avoid raw data sharing by communicating only model parameters, hence preserving data confidentiality.
Decentralized Learning Made Easy with DecentralizePy
Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance.
Boosting Federated Learning in Resource-Constrained Networks
GeL enables constrained edge devices to perform additional learning through guessed updates on top of gradient-based steps.
WideCaps: A Wide Attention based Capsule Network for Image Classification
However, on the datasets involving complex foreground and background regions such as CIFAR-10, the performance of the capsule network is sub-optimal due to its naive data routing policy and incompetence towards extracting complex features.
EinsteinPy: A Community Python Package for General Relativity
Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years.
General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics 83-04
Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss
In typical machine learning tasks and applications, it is necessary to obtain or create large labeled datasets in order to to achieve high performance.
Improved Training with Curriculum GANs
We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation.
Tackling the Story Ending Biases in The Story Cloze Test
The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning.
Optimizing for Generalization in Machine Learning with Cross-Validation Gradients
Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization performance.
Deep Learning Phase Segregation
Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems.
A Note on the Inception Score
Deep generative models are powerful tools that have produced impressive results in recent years.
