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Detection and attribution of vegetation greening trend in China over the last 30 years
Global Change Biology, 2015 The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge.
Shilong Piao, Lei Cheng, Yue Li
exaly +2 more sources
Improving Adversarial Transferability via Neuron Attribution-based Attacks [PDF]
Computer Vision and Pattern Recognition, 2022 Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs beforehand in security-sensitive applications.
Jianping Zhang +6 more
semanticscholar +1 more source
Impossibility theorems for feature attribution [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2022 Significance Machine learning models can learn complex patterns from data, but it is often difficult to understand why they make particular predictions.
Blair Bilodeau +3 more
semanticscholar +1 more source
Extreme weather impacts of climate change: an attribution perspective
Environmental Research: Climate, 2022 Extreme event attribution aims to elucidate the link between global climate change, extreme weather events, and the harms experienced on the ground by people, property, and nature.
B. Clarke +3 more
semanticscholar +1 more source
A framework for the detection and attribution of biodiversity change
Philosophical Transactions of the Royal Society of London. Biological Sciences, 2023 The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and high rates of compositional turnover have been reported worldwide.
Andrew Gonzalez +2 more
semanticscholar +1 more source
Do Feature Attribution Methods Correctly Attribute Features? [PDF]
AAAI Conference on Artificial Intelligence, 2021 Feature attribution methods are popular in interpretable machine learning. These methods compute the attribution of each input feature to represent its importance, but there is no consensus on the definition of "attribution", leading to many competing ...
Yilun Zhou +3 more
semanticscholar +1 more source
Geophysical Research Letters, 2023 While previous studies have suggested a substantial role of ozone‐depleting substances (ODSs) in historical climate change, their relative contribution to historical anthropogenic warming has not been quantified before.
M. Sigmond +5 more
doaj +1 more source
Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles
Data Science Journal, 2021 A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto ...
Jens Klump +5 more
doaj +1 more source
Self-Attention Attribution: Interpreting Information Interactions Inside Transformer [PDF]
AAAI Conference on Artificial Intelligence, 2020 The great success of Transformer-based models benefits from the powerful multi-head self-attention mechanism, which learns token dependencies and encodes contextual information from the input. Prior work strives to attribute model decisions to individual
Y. Hao, Li Dong, Furu Wei, Ke Xu
semanticscholar +1 more source
Pathways and pitfalls in extreme event attribution
Climatic Change, 2021 The last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event.
G. J. van Oldenborgh +10 more
semanticscholar +1 more source