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07/07/2023
STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map
Multi-Task Learning (MTL) is a powerful technique that has gained popula...
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03/22/2023
AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages
The advancement of speech technologies has been remarkable, yet its inte...
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09/12/2022
Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
Telemedicine utilization was accelerated during the COVID-19 pandemic, a...
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10/06/2021
Clustering Plotted Data by Image Segmentation
Clustering algorithms are one of the main analytical methods to detect p...
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06/24/2021
Meaningfully Explaining a Model's Mistakes
Understanding and explaining the mistakes made by trained models is crit...
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01/14/2021
Persistent Anti-Muslim Bias in Large Language Models
It has been observed that large-scale language models capture undesirabl...
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10/05/2020
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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03/08/2020
Improving Training on Noisy Stuctured Labels
Fine-grained annotations—e.g. dense image labels, image segmentation and...
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06/06/2019
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Accessibility is a major challenge of machine learning (ML). Typical ML ...
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02/12/2019
Contrastive Variational Autoencoder Enhances Salient Features
Variational autoencoders are powerful algorithms for identifying dominan...
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01/27/2019
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction
We introduce the concrete autoencoder, an end-to-end differentiable meth...
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10/31/2018
Contrastive Multivariate Singular Spectrum Analysis
We introduce Contrastive Multivariate Singular Spectrum Analysis, a nove...
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10/23/2018
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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04/02/2018
Stochastic EM for Shuffled Linear Regression
We consider the problem of inference in a linear regression model in whi...
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10/29/2017
Interpretation of Neural Networks is Fragile
In order for machine learning to be deployed and trusted in many applica...
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09/20/2017
Contrastive Principal Component Analysis
We present a new technique called contrastive principal component analys...
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05/03/2017