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Found 14 papers, 5 papers with code
Layer-wise Linear Mode Connectivity
Averaging neural network parameters is an intuitive method for fusing the knowledge of two independent models.
FAM: Relative Flatness Aware Minimization
Their widespread adoption in practice, though, is dubious because of the lack of theoretically grounded connection between flatness and generalization, in particular in light of the reparameterization curse - certain reparameterizations of a neural network change most flatness measures but do not change generalization.
Guideline for Trustworthy Artificial Intelligence -- AI Assessment Catalog
Artificial Intelligence (AI) has made impressive progress in recent years and represents a key technology that has a crucial impact on the economy and society.
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation
Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge.
Novelty Detection in Sequential Data by Informed Clustering and Modeling
We propose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately.
Resource-Constrained On-Device Learning by Dynamic Averaging
This paper investigates an approach to communication-efficient on-device learning of integer exponential families that can be executed on low-power processors, is privacy-preserving, and effectively minimizes communication.
Relative Flatness and Generalization
Flatness of the loss curve is conjectured to be connected to the generalization ability of machine learning models, in particular neural networks.
A Reparameterization-Invariant Flatness Measure for Deep Neural Networks
The performance of deep neural networks is often attributed to their automated, task-related feature construction.
Information-Theoretic Perspective of Federated Learning
An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model.
Feature-Robustness, Flatness and Generalization Error for Deep Neural Networks
With this, the generalization error of a model trained on representative data can be bounded by its feature robustness which depends on our novel flatness measure.
System Misuse Detection via Informed Behavior Clustering and Modeling
One of the main tasks of cybersecurity is recognizing malicious interactions with an arbitrary system.
Introducing Noise in Decentralized Training of Neural Networks
It has been shown that injecting noise into the neural network weights during the training process leads to a better generalization of the resulting model.
Making Efficient Use of a Domain Expert's Time in Relation Extraction
In this paper, we report on an alternative approach where we first construct a relation extraction model using distant supervision, and only later make use of a domain expert to refine the results.
Efficient Decentralized Deep Learning by Dynamic Model Averaging
We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources.
