Inter- and intra-year forest change detection and monitoring of aboveground biomass dynamics using Sentinel-2 and Landsat
Flavie Pelletier
Michael A. Wulder
Joanne C. White
Txomin Hermosilla
Inter- and intra-year forest change detection and monitoring of aboveground biomass dynamics using Sentinel-2 and Landsat
Flavie Pelletier
Michael A. Wulder
Joanne C. White
Txomin Hermosilla
Inter- and intra-year forest change detection and monitoring of aboveground biomass dynamics using Sentinel-2 and Landsat
Flavie Pelletier
Michael A. Wulder
Joanne C. White
Txomin Hermosilla
Inter- and intra-year forest change detection and monitoring of aboveground biomass dynamics using Sentinel-2 and Landsat
Flavie Pelletier
Michael A. Wulder
Joanne C. White
Txomin Hermosilla
Investigating Generalization Behaviours of Generative Flow Networks
Lazar Atanackovic
Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spa… (see more)ces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favourable generalization properties. In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets. In particular, we find that the functions that GFlowNets learn to approximate have an implicit underlying structure which facilitate generalization. We also find that GFlowNets are sensitive to being trained offline and off-policy; however, the reward implicitly learned by GFlowNets is robust to changes in the training distribution.
Linking biodiversity, ecosystem function, and Nature's contributions to people: a macroecological energy flux perspective.
Ana Carolina Antunes
Emilio Berti
Ulrich Brose
Myriam R. Hirt
Dirk N. Karger
Louise M. J. O'Connor
Wilfried Thuiller
Benoit Gauzens
Linking biodiversity, ecosystem function, and Nature’s contributions to people: a macroecological energy flux perspective
Ana Carolina Antunes
Emilio Berti
Ulrich Brose
Myriam R. Hirt
Dirk N. Karger
Louise M. J. O'Connor
Wilfried Thuiller
Benoit Gauzens
Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source
Ariane Marelli
Chao Li
Aihua Liu
Hanh Nguyen
Harry Moroz
James M. Brophy
Liming Guo
Model Collapse Demystified: The Case of Regression
Yunzhen Feng
Julia Kempe
In the era of proliferation of large language and image generation models, the phenomenon of "model collapse" refers to the situation whereb… (see more)y as a model is trained recursively on data generated from previous generations of itself over time, its performance degrades until the model eventually becomes completely useless, i.e the model collapses. In this work, we study this phenomenon in the setting of high-dimensional regression and obtain analytic formulae which quantitatively outline this phenomenon in a broad range of regimes. In the special case of polynomial decaying spectral and source conditions, we obtain modified scaling laws which exhibit new crossover phenomena from fast to slow rates. We also propose a simple strategy based on adaptive regularization to mitigate model collapse. Our theoretical results are validated with experiments.
Neural semantic tagging for natural language-based search in building information models: Implications for practice
Mehrzad Shahinmoghadam
Ali Motamedi
Properties and Styles of Software Technology Tutorials
Deeksha M. Arya
Martin P. Robillard
A large number of tutorials for popular software development technologies are available online, and those about the same technology vary wid… (see more)ely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. resources, differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their distinguishing attributes, i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its resource style, i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.
Properties and Styles of Software Technology Tutorials
Deeksha M. Arya
Martin P. Robillard
A large number of tutorials for popular software development technologies are available online, and those about the same technology vary wid… (see more)ely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. resources, differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their distinguishing attributes, i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its resource style, i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.