Leep: A new measure to evaluate transferability of learned representations
We introduce a new measure to evaluate the transferability of representations learned by
classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy …
classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy …
[HTML][HTML] Wasserstein task embedding for measuring task similarities
Measuring similarities between different tasks is critical in a broad spectrum of machine
learning problems, including transfer, multi-task, continual, and meta-learning. Most current …
learning problems, including transfer, multi-task, continual, and meta-learning. Most current …
Feature-based distant domain transfer learning
In this paper, we study a not well-investigated but important transfer learning problem
termed Distant Domain Transfer Learning (DDTL). This topic is closely related to negative …
termed Distant Domain Transfer Learning (DDTL). This topic is closely related to negative …
Cross-modality transfer learning for image-text information management
In the past decades, information from all kinds of data has been on a rapid increase. With
state-of-the-art performance, machine learning algorithms have been beneficial for …
state-of-the-art performance, machine learning algorithms have been beneficial for …
Which model to transfer? finding the needle in the growing haystack
Transfer learning has been recently popularized as a data-efficient alternative to training
models from scratch, in particular for computer vision tasks where it provides a remarkably …
models from scratch, in particular for computer vision tasks where it provides a remarkably …
[HTML][HTML] Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation
The use of transfer learning (TL) techniques has become common practice in fields such as
computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge …
computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge …
Assessing the value of transfer learning metrics for RF domain adaptation
The use of transfer learning (TL) techniques has become common practice in fields such as
computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge …
computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge …
A partition-based similarity for classification distributions
Herein we define a measure of similarity between classification distributions that is both
principled from the perspective of statistical pattern recognition and useful from the …
principled from the perspective of statistical pattern recognition and useful from the …
Task Fingerprinting for Meta Learning inBiomedical Image Analysis
Shortage of annotated data is one of the greatest bottlenecks in biomedical image analysis.
Meta learning studies how learning systems can increase in efficiency through experience …
Meta learning studies how learning systems can increase in efficiency through experience …
Toward a Human-in-the-Loop Approach to Create Training Datasets for RDF Lexicalisation
Datasets that include alignments between natural language and Knowledge Graphs are
fundamental to a wide variety of Natural Language Processing and Generation tasks …
fundamental to a wide variety of Natural Language Processing and Generation tasks …