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Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
TLDR
The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% and real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.
Embedding Propagation: Smoother Manifold for Few-Shot Classification
TLDR
This work empirically shows that embedding propagation yields a smoother embedding manifold, and shows that applying embedding propagate to a transductive classifier achieves new state-of-the-art results in mini-Imagenet, tiered-Imageet, Imagenet-FS, and CUB.
Interpretable genotype-to-phenotype classifiers with performance guarantees
TLDR
This work proposes strong performance guarantees, based on sample compression theory, for rule-based learning algorithms that produce highly interpretable models and shows that these guarantees can be leveraged to accelerate learning and improve model interpretability.
Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons
TLDR
The method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations, and is applicable to a variety of organisms and phenotypes.
Differentiable Causal Discovery from Interventional Data
TLDR
This work proposes a neural network-based method for discovering causal relationships in data that can leverage interventional data and illustrates the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows.
In Search of Robust Measures of Generalization
TLDR
This work addresses the question of how to evaluate generalization bounds empirically and argues that generalization measures should instead be evaluated within the framework of distributional robustness.
Learning a peptide-protein binding affinity predictor with kernel ridge regression
TLDR
For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy, and outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities.
Deep Learning for Electromyographic Hand Gesture Signal Classification by Leveraging Transfer Learning
TLDR
A convolutional network (ConvNet) is augmented with transfer learning techniques to leverage inter-user data from the first dataset, alleviating the burden imposed on a single individual to generate a vast quantity of training data for sEMG-based gesture recognition.
Synbols: Probing Learning Algorithms with Synthetic Datasets
TLDR
The tool's high-level interface provides a language for rapidly generating new distributions on the latent features, including various types of textures and occlusions, and to showcase the versatility of Synbols, it is used to dissect the limitations and flaws in standard learning algorithms in various learning setups.
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