Search Results for author:
Found 11 papers, 8 papers with code
OAM-TCD: A globally diverse dataset of high-resolution tree cover maps
Using our dataset, we train reference instance and semantic segmentation models that compare favorably to existing state-of-the-art models.
GEO-Bench: Toward Foundation Models for Earth Monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
We present DataScope (ease. ml/datascope), the first system that efficiently computes Shapley values of training examples over an end-to-end ML pipeline, and illustrate its applications in data debugging for ML training.
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery
This proposal paper describes the first systematic comparison of forest carbon estimation from aerial imagery, satellite imagery, and ground-truth field measurements via deep learning-based algorithms for a tropical reforestation project.
TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
Recent works propose low-cost and accurate MRV via automatically determining forest carbon from drone imagery, collected by the landowners.
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption.
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms
The most surprising result is that for unweighted $K$NN classifiers and regressors, the Shapley value of all $N$ data points can be computed, exactly, in $O(N\log N)$ time -- an exponential improvement on computational complexity!
Towards Efficient Data Valuation Based on the Shapley Value
In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in cooperative game theory.
DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation
We illustrate that trusted computation can enable the creation of an AI market, where each data point has an exact value that should be paid to its creator.
