ART improves transfer learning performance with robust theory and methods.
problem Improving performance of primary tasks using auxiliary data.
method Adaptive Robust Transfer Learning (ART) pipeline with theoretical guarantees.
result ART provides a provable theoretical guarantee for adaptive transfer and robustness.
Adversarial perturbations fool wearable sensor systems, showing transferability across different systems.
problem Adversarial examples fool wearable sensor systems, showing transferability across different systems.
method Study of adversarial transferability in wearable sensor systems from four perspectives: systems, subjects, sensor body locations, and datasets.
result Strong untargeted transferability in most cases, targeted attacks less successful.
New research on limits of transfer learning, proving key selection and dependence requirements.
problem Insufficient theoretical foundation for transfer learning.
method Proved novel results on transfer learning, emphasizing selection of information and dependence between domains.
result Upper bound on improvement possible with transfer learning, highlighting the need for careful selection.
UDA learns target domain from unlabeled data via source knowledge transfer.
problem Learning unlabeled target domain data.
method Transferring both source knowledge and target-relatedness.
result Improves UDA performance by simultaneous knowledge transfer.
Despite the recent success of deep transfer learning approaches in NLP, there is a lack of quantitative studies demonstrating the gains these models offer in low-shot text classification tasks over existing paradigms. Deep transfer learning approaches such as BERT and ULMFiT demonstrate that they can beat state-of-the-…
Transfer learning improves sparse, interpretable probabilistic classification.
problem Sparse and interpretable models in transfer learning.
method Two transfer learning extensions integrated into sparse and interpretable probabilistic classification vector machine.
result Transfer learning extensions improve sparsity and performance.
Study measures impact of data and neural net similarity on transferability in restaurant sales data.
problem Identify indicators for successful transferability of neural nets across different data sets.
method Empirical study on sales data from six restaurants, calculating indicators based on data and neural net similarities.
result Negative correlations between transferability and indicators, allowing better model performance and fewer transfers.
Optimal Transport enhances machine learning with new methods.
problem Comparing and manipulating probability distributions in machine learning.
method Probabilistic framework rooted in rich history and theory.
result New solutions in generative modeling and transfer learning.
Meta-Learning is a subarea of Machine Learning that aims to take advantage of prior knowledge to learn faster and with fewer data [1]. There are different scenarios where meta-learning can be applied, and one of the most common is algorithm recommendation, where previous experience on applying machine learning algorith…
Recent years, transfer learning has attracted much attention in the community of machine learning. In this paper, we mainly focus on the tasks of parameter transfer under the framework of extreme learning machine (ELM). Unlike the existing parameter transfer approaches, which incorporate the source model information in…
Transfer learning improves machine learning models for equipment diagnostics.
problem Limited model performance due to training data mismatch.
method Transfer learning to reuse knowledge from similar tasks.
result Transfer learning enhances model applicability in PHM.
Physics-informed neural networks simulate radiative transfer efficiently.
problem Simulating radiative transfer accurately and efficiently.
method Physics-informed neural networks trained to minimize radiative transfer equations.
result PINNs provide an easy-to-implement, robust, and accurate method for radiative transfer simulation.
This paper reviews transfer learning for financial data predictions, highlighting its potential.
problem Accurate stock price prediction in financial time series is challenging due to noise and non-linear relationships.
method Transfer Learning applied to financial market predictions.
result Transfer Learning can improve financial prediction capability.
Bayesian methods improve learning from related domains.
problem Finding optimal information to transfer between domains.
method Bayesian approaches to transfer learning.
result Bayesian methods outperform frequentist competitors in simulations.
Survey on negative transfer in machine learning.
problem Negative transfer in transfer learning reduces target domain performance.
method Systematic review of 50+ approaches to mitigate negative transfer.
result Lack of systematic survey on negative transfer.
Transfer learning improves portfolio optimization by identifying transfer risk.
problem Financial portfolio optimization problem.
method Introduces transfer risk concept within transfer learning framework.
result Transfer risk is a significant indicator of transferability and enhances portfolio management efficiency.
Improved machine learning model performance through data augmentation, custom loss functions, and transfer learning.
problem Poor performance of a traditional engineering model due to limited training data.
method Data augmentation, custom loss functions, transfer learning.
result Improvement of at least 38% in performance across five models.
New technique explains convergence in ML models with data modifications.
problem Understanding convergence of ML models under data changes.
method Analogue of Fatou's lemma and gamma-convergence.
result Relevance and applications in general ML tasks and domain adaptation.
Transfer learning improves causal model estimates in small samples.
problem Challenges in estimating individual treatment effects (ITE) from small datasets.
method Treatment Agnostic Representation Networks (TARNet) with transfer learning (TL-TARNet).
result Transfer learning reduces ITE error and bias in small samples.
New analysis shows transfer learning can significantly reduce sample size for complex models.
problem Reducing sample size needed for complex models like large language models.
method Optimal transport viewpoint applied to analyze transfer learning efficiency.
result Transfer learning can achieve better sample efficiency for complex models.
New active learning method uses combinatorial coverage to improve data transfer and reduce bias.
problem Inability to transfer sampled data to new models and sampling bias issues.
method Data-centric active learning methods utilizing combinatorial coverage.
result Sampling data with coverage leads to better data transfer and competitive sampling bias.
Proposes a multi-fidelity machine learning strategy integrating low-fidelity deterministic and high-fidelity Bayesian models.
problem Addressing the accuracy-efficiency trade-off in machine learning with scarce high-fidelity data.
method Integrates a non-probabilistic regression model for low-fidelity with a Bayesian model for high-fidelity, trained in a staggered scheme.
result Achieves comparable performance in mean and uncertainty estimation with reduced training time and effective mitigation of overfitting.
This research evaluates learning models for bionic robots, focusing on transfer function identification.
problem Developers need guidance on selecting and constructing transfer functions for bionic robots.
method Comprehensive evaluation strategy including data collection, learning model selection, comparative analysis, and transfer function identification.
result A framework for effectively dealing with multi-input multi-output robotic data.
SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.
problem AI exclusion of SMEs due to data scale mismatch.
method Bayesian transfer learning with hierarchical pooling and conformal prediction.
result 96.7% AUC on 100 obs SMEs, 24.2 point improvement over logistic regression.
New method transfers emotions in facial images.
problem Transforming facial images to different emotions.
method Infinite task learning and vector-valued reproducing kernel Hilbert spaces.
result Achieves low reconstruction cost and high emotion classification accuracy.
A new machine learning framework reduces IoT data transfer by two orders of magnitude.
problem Reducing data transfer in IoT devices over wireless channels.
method Developed a machine learning framework for distributed functional compression over GMAC and AWGN channels.
result The framework reduces communication by two orders of magnitude compared to cloud-based methods.
Paper evaluates membership inference attacks on transfer learning models.
problem Evaluating membership inference attacks on transfer learning models.
method Shadow model training strategy to derive data for membership inference classifier.
result Membership inference attacks can achieve effective performance against transfer learning models.
Improves transfer learning by weighting importance based on test-over-training density.
problem Distribution shift in training and test data.
method Joint and dynamic importance-predictor estimation, causal mechanism transfer.
result Enhanced transfer learning performance in complex, high-dimensional tasks.
Deep transfer learning boosts single-EEG arousal detection accuracy.
problem Challenges in machine learning due to channel mismatch across different EEG datasets.
method Transfer learning strategy to adapt a multivariate model to single-channel EEG data.
result Fine-tuned model achieves similar performance to baseline model and significantly outperforms a single-channel model.
The paper investigates what enables successful transfer learning and separates feature reuse from data statistics.
problem Understanding what enables successful transfer learning and identifying the responsible parts of the network.
method Analyzes transfer learning on block-shuffled images to distinguish feature reuse from data statistics.
result Some benefit of transfer learning comes from learning low-level statistics of data, not just feature reuse.
Autoencoders improve feature extraction for aviation ML tasks.
problem Manual feature processing is labor-intensive, inefficient, and prone to loss.
method Unsupervised learning using autoencoders to extract features.
result Autoencoders can automatically extract effective features and reduce data cleaning workload.
BAR reprograms black-box ML models for transfer learning with scarce data.
problem Transfer learning with limited data and resources.
method Zeroth-order optimization and multi-label mapping techniques to reprogram black-box models.
result BAR outperforms state-of-the-art methods and baseline transfer learning approaches.
Machine learning techniques are being applied to scientific fields, showing promise and challenges.
problem Applying machine learning to scientific data poses challenges in universality and robustness.
method Critical analysis of anomaly detection techniques, focusing on data universality, robustness, and transferability.
result Machine learning techniques show potential but also present domain-specific challenges.
A new machine-learned CG model predicts protein structures efficiently.
problem Developing a universal, computationally efficient protein simulation model.
method Combining deep learning with all-atom protein simulations to create a transferable CG force field.
result The model predicts protein structures, intermediates, and fluctuations efficiently.
Pre-training a deep neural network on the ImageNet dataset is a common practice for training deep learning models, and generally yields improved performance and faster training times. The technique of pre-training on one task and then retraining on a new one is called transfer learning. In this paper we analyse the eff…
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prosp…
Transfer learning has received a lot of attention in the machine learning community over the last years, and several effective algorithms have been developed. However, relatively little is known about their theoretical properties, especially in the setting of lifelong learning, where the goal is to transfer information…
Automated machine learning aims to automate the whole process of machine learning, including model configuration. In this paper, we focus on automated hyperparameter optimization (HPO) based on sequential model-based optimization (SMBO). Though conventional SMBO algorithms work well when abundant HPO trials are availab…
Survey on using large models to train smaller datasets in NLP.
problem Lack of large datasets and computing resources for NLP tasks.
method Analysis of recent transfer learning approaches in NLP.
result Increased demand for transfer learning in NLP due to large models.
Transfer learning is a recent field of machine learning research that aims to resolve the challenge of dealing with insufficient training data in the domain of interest. This is a particular issue with traditional deep neural networks where a large amount of training data is needed. Recently, StochasticNets was propose…
Machine learning speeds up CRTM model predictions for weather forecasting.
problem Insufficient computational efficiency of radiative transfer models.
method Probabilistic neural network emulator of CRTM.
result Emulator predicts brightness temperatures with RMSE < 0.1 K for clear sky conditions.
Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfe…
When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We develop a new hyperparameter-free ensemble model for Bayesian optimization that is a generalization of two existing transfer learning extens…
Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). This paper presents two machine learning approaches, transfer learning and selection transfer, that are compared upon their…
In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. However, in many healthcare applications it is difficult…
Transfer learning improves MNI's performance in high-dimensional linear regression.
problem Improving model performance in high-dimensional linear regression with diverse data.
method Proposes a Transfer MNI approach, analyzing its excess risk and conditions for outperformance.
result Identifies free-lunch covariate shift regimes where knowledge transfer benefits.
This paper introduces a new transfer learning method for regression.
problem Boosting predictive power in scenarios with scarce data.
method Affine model transfer, following expected-square loss minimization.
result The affine model transfer method encompasses various existing methods and clarifies its theoretical properties.
Brain imaging data are important in brain sciences yet expensive to obtain, with big volume (i.e., large p) but small sample size (i.e., small n). To tackle this problem, transfer learning is a promising direction that leverages source data to improve performance on related, target data. Most transfer learning methods …