Fetching the paper…
Reading the bibliography…
Labeled datasets for agriculture are extremely spatially imbalanced.
Improving machine learning performance by removing redundant cases in medical data sets
Ohno-Machado, L., Fraser, H. S., and Ohrn, A · 1998
Earlier work this paper cites.
Learning to Learn
Thrun, S. and Pratt, L · 1998
Earlier work this paper cites.
Visualizing high-dimensional data using t-SNE
van der Maaten, L. and Hinton, G · 2008
Earlier work this paper cites.
MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
Earlier work this paper cites.
Deep Gaussian processes
Damianou, A. and Lawrence, N. D · 2013
Earlier work this paper cites.
The impact of conflict and political instability on agricultural investments in Mali and Nigeria
Kimenyi, M., Adibe, J., Djiré, M., Jirgi, A. J., Kergna, A., Deressa, T. T., Pugliese, J. E., and Westbury, A · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
MODIS/terra surface reflectance 8-day l3 global 500m SIN grid v006, 2015
Vermote, E · 2015
Earlier work this paper cites.
MODIS/aqua land surface temperature/emissivity 8-day l3 global 1km SIN grid v006, 2015
Wan, Z., Hook, S., and Hulley, G · 2015
Earlier work this paper cites.
Gaussian error linear units (GELUs)
Hendrycks, D. and Gimpel, K · 2016
Earlier work this paper cites.
Combining satellite imagery and machine learning to predict poverty
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and Ermon, S · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
Earlier work this paper cites.
No classification without representation: Assessing geodiversity issues in open data sets for the developing world
Shankar, S., Halpern, Y., Breck, E., Atwood, J., Wilson, J., and Sculley, D · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Earlier work this paper cites.
Deep gaussian process for crop yield prediction based on remote sensing data
You, J., Li, X., Low, M., Lobell, D., and Ermon, S · 2017
Cited alongside, same era.
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 2018
Cited alongside, same era.
Deep transfer learning for crop yield prediction with remote sensing data
Wang, A. X., Tran, C., Desai, N., Lobell, D., and Ermon, S · 2018
Cited alongside, same era.
Group normalization, 2018
Wu, Y. and He, K · 2018
Cited alongside, same era.
How to train your MAML
Antoniou, A., Edwards, H., and Storkey, A · 2019
Cited alongside, same era.
Chimera: A multi-task recurrent convolutional neural network for forest classification and structural estimation
Chang, T., Rasmussen, B. P., Dickson, B. G., and Zachmann, L. J · 2019
Task-robust model-agnostic meta-learning
Collins, L., Mokhtari, A., and Shakkottai, S · 2020
Later among the works it cites.
Rapid response crop maps in data sparse regions
Kerner, H., Tseng, G., Becker-Reshef, I., Nakalembe, C., Barker, B., Munshell, B., Paliyam, M., and Hosseini, M · 2020
Later among the works it cites.
Deep remote sensing methods for methane detection in overhead hyperspectral imagery
Kumar, S., Torres, C., Ulutan, O., Ayasse, A., Roberts, D., and Manjunath, B · 2020
Later among the works it cites.
Cropland expansion in the united states produces marginal yields at high costs to wildlife
Lark, T. J., Spawn, S. A., Bougie, M., and Gibbs, H. K · 2020
Later among the works it cites.
Meta-learning for few-shot land cover classification
Rußwurm, M., Wang, S., Korner, M., and Lobell, D · 2020
Later among the works it cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Does object recognition work for everyone?
de Vries, T., Misra, I., Wang, C., and van der Maaten, L · 2019
Cited alongside, same era.
Task agnostic meta-learning for few-shot learning
Jamal, M. A. and Qi, G.-J · 2019
Cited alongside, same era.
Country-wide high-resolution vegetation height mapping with sentinel-2
Lang, N., Schindler, K., and Wegner, J. D · 2019
Cited alongside, same era.
Presence-Only Geographical Priors for Fine-Grained Image Classification
Mac Aodha, O., Cole, E., and Perona, P · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
Multimodal model-agnostic meta-learning via task-aware modulation
Vuorio, R., Sun, S.-H., Hu, H., and Lim, J. J · 2019
Cited alongside, same era.
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Evci, U., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., and Larochelle, H · 2020
Later among the works it cites.
Weakly supervised deep learning for segmentation of remote sensing imagery
Wang, S., Chen, W., Xie, S. M., Azzari, G., and Lobell, D · 2020
Later among the works it cites.
Map-net: Multiple attending path neural network for building footprint extraction from remote sensed imagery
Zhu, Q., Liao, C., Hu, H., Mei, X., and Li, H · 2020
Later among the works it cites.
Species distribution modeling for machine learning practitioners: A review
Beery, S., Cole, E., Parker, J., Perona, P., and Winner, K · 2021
Later among the works it cites.
Hurricane forecasting: A novel multimodal machine learning framework
Boussioux, L., Zeng, C., Bertsimas, D., and Guenais, T. J · 2021
Later among the works it cites.
Slimml: Removing non-critical input data in large-scale iterative machine learning
Han, R., Liu, C. H., Li, S., Chen, L. Y., Wang, G., Tang, J., and Ye, J · 2021
Later among the works it cites.
Massive soybean expansion in south america since 2000 and implications for conservation
Song, X.-P., Hansen, M. C., Potapov, P., Adusei, B., Pickering, J., Adami, M., Lima, A., Zalles, V., Stehman, S. V., Di Bella, C. M., Conde, M. C., Copati, E. J., Fernandes, L. B., Hernandez-Serna, A., Jantz, S. M., Pickens, A. H., Turubanova, S., and Tyukavina, A · 2021
Later among the works it cites.
Learning a universal template for few-shot dataset generalization
Triantafillou, E., Larochelle, H., Zemel, R., and Dumoulin, V · 2021
Later among the works it cites.
Crop mapping from image time series: Deep learning with multi-scale label hierarchies
Turkoglu, M. O., D’Aronco, S., Perich, G., Liebisch, F., Streit, C., Schindler, K., and Wegner, J. D · 2021
Later among the works it cites.
Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning
Yeh, C., Meng, C., Wang, S., Driscoll, A., Rozi, E., Liu, P., Lee, J., Burke, M., Lobell, D. B., and Ermon, S · 2021
Later among the works it cites.