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Found 21 papers, 9 papers with code
Transfer Learning in ECG Diagnosis: Is It Effective?
The adoption of deep learning in ECG diagnosis is often hindered by the scarcity of large, well-labeled datasets in real-world scenarios, leading to the use of transfer learning to leverage features learned from larger datasets.
Explainable Severity ranking via pairwise n-hidden comparison: a case study of glaucoma
Primary open-angle glaucoma (POAG) is a chronic and progressive optic nerve condition that results in an acquired loss of optic nerve fibers and potential blindness.
Simple Transferability Estimation for Regression Tasks
We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task.
MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Analysis
We introduce MELEP, which stands for Muti-label Expected Log of Empirical Predictions, a novel measure to estimate how effective it is to transfer knowledge from a pre-trained model to a downstream task in a multi-label settings.
Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation
In this work, we propose a data augmentation approach that addresses the problem of lack of data for online hate speech detection in limited data contexts using synthetic data generation techniques.
EnSolver: Uncertainty-Aware CAPTCHA Solver Using Deep Ensembles
The popularity of text-based CAPTCHA as a security mechanism to protect websites from automated bots has prompted researches in CAPTCHA solvers, with the aim of understanding its failure cases and subsequently making CAPTCHAs more secure.
Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification
Transfer learning plays an essential role in Deep Learning, which can remarkably improve the performance of the target domain, whose training data is not sufficient.
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy
We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task.
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models.
An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator
In this paper, we perform an empirical study on the impact of several loss functions on the performance of standard GAN models, Deep Convolutional Generative Adversarial Networks (DCGANs).
LEEP: A New Measure to Evaluate Transferability of Learned Representations
We introduce a new measure to evaluate the transferability of representations learned by classifiers.
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Transferability and Hardness of Supervised Classification Tasks
As a case study, we transfer a learned face recognition model to CelebA attribute classification tasks, showing state of the art accuracy for tasks estimated to be highly transferable.
Toward Understanding Catastrophic Forgetting in Continual Learning
As an application, we apply our procedure to study two properties of a task sequence: (1) total complexity and (2) sequential heterogeneity.
Bayesian Active Learning With Abstention Feedbacks
We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate.
Improving and Understanding Variational Continual Learning
In the continual learning setting, tasks are encountered sequentially.
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Second, the granularity of the updates e. g. whether the updates are local to each data point and employ message passing or global.
Variational Continual Learning
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks.
Bayesian Pool-based Active Learning With Abstention Feedbacks
We study pool-based active learning with abstention feedbacks, where a labeler can abstain from labeling a queried example with some unknown abstention rate.
Streaming Sparse Gaussian Process Approximations
Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped.
Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization
Avoiding this devise, we propose an accelerated randomized mirror descent method for solving this problem without the strongly convex assumption.
