Search Results for author:
Found 23 papers, 12 papers with code
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models.
Protecting Sensitive Data through Federated Co-Training
Federated learning allows us to collaboratively train a model without pooling the data by iteratively aggregating the parameters of local models.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
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.
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
It is an open secret that ImageNet is treated as the panacea of pretraining.
Open-Source Skull Reconstruction with MONAI
The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework.
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features
In this work, we propose a unified framework to tackle the non-iid issues for internal and external clients together.
Federated Learning from Small Datasets
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data.
Picking Daisies in Private: Federated Learning from Small Datasets
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data.
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.
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.
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.
Communication-Efficient Distributed Online Learning with Kernels
It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion.
Adaptive Communication Bounds for Distributed Online Learning
The learning performance of such a protocol is intuitively optimal if approximately the same loss is incurred as in a hypothetical serial setting.
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.
Corresponding Projections for Orphan Screening
We propose a novel transfer learning approach for orphan screening called corresponding projections.
Effective Parallelisation for Machine Learning
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications.
Efficient Decentralized Deep Learning by Dynamic Model Averaging
We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources.
