
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
The efficacy of a drug depends on its binding affinity to the therapeuti...
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Improving Training on Noisy Stuctured Labels
Finegrained annotations—e.g. dense image labels, image segmentation and...
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Gradio: HassleFree Sharing and Testing of ML Models in the Wild
Accessibility is a major challenge of machine learning (ML). Typical ML ...
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Contrastive Variational Autoencoder Enhances Salient Features
Variational autoencoders are powerful algorithms for identifying dominan...
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Concrete Autoencoders for Differentiable Feature Selection and Reconstruction
We introduce the concrete autoencoder, an endtoend differentiable meth...
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Contrastive Multivariate Singular Spectrum Analysis
We introduce Contrastive Multivariate Singular Spectrum Analysis, a nove...
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Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
Measuring similarities between unlabeled time series trajectories is an ...
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Stochastic EM for Shuffled Linear Regression
We consider the problem of inference in a linear regression model in whi...
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Interpretation of Neural Networks is Fragile
In order for machine learning to be deployed and trusted in many applica...
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Contrastive Principal Component Analysis
We present a new technique called contrastive principal component analys...
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Linear Regression with Shuffled Labels
Is it possible to perform linear regression on datasets whose labels are...
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Abubakar Abid
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