Search Results for author: Sharon Zhou

Found 11 papers, 4 papers with code

ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery

no code implementations11 Nov 2020 Jeremy Irvin, Hao Sheng, Neel Ramachandran, Sonja Johnson-Yu, Sharon Zhou, Kyle Story, Rose Rustowicz, Cooper Elsworth, Kemen Austin, Andrew Y. Ng

Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies.

General Classification

Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models

no code implementations9 Oct 2020 Eric Zelikman, Sharon Zhou, Jeremy Irvin, Cooper Raterink, Hao Sheng, Anand Avati, Jack Kelly, Ram Rajagopal, Andrew Y. Ng, David Gagne

Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

1 code implementation ICLR 2021 Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar Carlsson, Stefano Ermon

Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models.

CRUDE: Calibrating Regression Uncertainty Distributions Empirically

no code implementations26 May 2020 Eric Zelikman, Christopher Healy, Sharon Zhou, Anand Avati

Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting.

Autonomous Vehicles General Classification

Data augmentation with Mobius transformations

1 code implementation7 Feb 2020 Sharon Zhou, Jiequan Zhang, Hang Jiang, Torbjorn Lundh, Andrew Y. Ng

Data augmentation has led to substantial improvements in the performance and generalization of deep models, and remain a highly adaptable method to evolving model architectures and varying amounts of data---in particular, extremely scarce amounts of available training data.

Data Augmentation Translation

Approximating Human Judgment of Generated Image Quality

no code implementations30 Nov 2019 Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell Gordon, Stefano Ermon

Generative models have made immense progress in recent years, particularly in their ability to generate high quality images.

Establishing an Evaluation Metric to Quantify Climate Change Image Realism

no code implementations22 Oct 2019 Sharon Zhou, Alexandra Luccioni, Gautier Cosne, Michael S. Bernstein, Yoshua Bengio

Because metrics for comparing the realism of different modes in a conditional generative model do not exist, we propose several automated and human-based methods for evaluation.

Humanitarian

HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

no code implementations NeurIPS 2019 Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael S. Bernstein

We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time.

Image Generation Unconditional Image Generation

HYPE: Human-eYe Perceptual Evaluation of Generative Models

no code implementations ICLR Workshop DeepGenStruct 2019 Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Durim Morina, Michael S. Bernstein

The second, HYPE-Infinity, measures human error rate on fake and real images with no time constraints, maintaining stability and drastically reducing time and cost.

Image Generation Unconditional Image Generation

Countdown Regression: Sharp and Calibrated Survival Predictions

1 code implementation21 Jun 2018 Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Ng

Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.

Decision Making Mortality Prediction +1

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