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Found 13 papers, 3 papers with code
Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation
Multi-Source Domain Adaptation (MSDA) is a challenging scenario where multiple related and heterogeneous source datasets must be adapted to an unlabeled target dataset.
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
In this paper, we tackle Multi-Source Domain Adaptation (MSDA), a task in transfer learning where one adapts multiple heterogeneous, labeled source probability measures towards a different, unlabeled target measure.
Optimal Transport for Domain Adaptation through Gaussian Mixture Models
The optimal transport solution gives us a matching between source and target domain mixture components.
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning
To solve it, we adapt previous works in the MSDA literature, such as Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD method Distribution Matching.
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions.
Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process
In system monitoring, automatic fault diagnosis seeks to infer the systems' state based on sensor readings, e. g., through machine learning models.
Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions.
Recent Advances in Optimal Transport for Machine Learning
This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning, such as generative modeling and transfer learning.
Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features
The performance gap is reduced with bigger datasets, but considering the increased computational load, end-to-end training may not be profitable for this application.
Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance
In this study, we investigated the impact of long-term recordings on motor imagery decoding from two main perspectives: model requirements regarding dataset size and potential for patient adaptation.
Decoding ECoG signal into 3D hand translation using deep learning
These models have a limited representational capacity and may fail to capture the relationship between ECoG signal and continuous hand movements.
On the Needs for Rotations in Hypercubic Quantization Hashing
The aim of this paper is to endow the well-known family of hypercubic quantization hashing methods with theoretical guarantees.
Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization
We demonstrate the quality of our binary sketches through experiments on real data for the nearest neighbors search task in the online setting.
