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A Maximal Correlation Approach to Imposing Fairness in Machine Learning
As machine learning algorithms grow in popularity and diversify to many ...
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Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Transparency of algorithmic systems entails exposing system properties t...
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Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling
Current autoencoder-based disentangled representation learning methods a...
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not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget
BigGAN is the state-of-the-art in high-resolution image generation, succ...
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Optimizing Mode Connectivity via Neuron Alignment
The loss landscapes of deep neural networks are not well understood due ...
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AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Action recognition is an open and challenging problem in computer vision...
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OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Data augmentation is one of the most important tools in training modern ...
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Fair Data Integration
The use of machine learning (ML) in high-stakes societal decisions has e...
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Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models
The wide-spread adoption of representation learning technologies in clin...
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StarNet: towards weakly supervised few-shot detection and explainable few-shot classification
In this paper, we propose a new few-shot learning method called StarNet,...
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TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
The field of Few-Shot Learning (FSL), or learning from very few (typical...
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Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration
The hypothesis that sub-network initializations (lottery) exist within t...
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Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
More than 200 generic drugs approved by the U.S. Food and Drug Administr...
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Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets
Recent advances in computer vision and deep learning have led to breakth...
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Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
With rapid adoption of deep learning in high-regret applications, the qu...
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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
As artificial intelligence and machine learning algorithms make further ...
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Generating Contrastive Explanations with Monotonic Attribute Functions
Explaining decisions of deep neural networks is a hot research topic wit...
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Understanding Unequal Gender Classification Accuracy from Face Images
Recent work shows unequal performance of commercial face classification ...
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Co-regularized Alignment for Unsupervised Domain Adaptation
Deep neural networks, trained with large amount of labeled data, can fai...
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AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Fairness is an increasingly important concern as machine learning models...
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Can Deep Clinical Models Handle Real-World Domain Shifts?
The hypothesis that computational models can be reliable enough to be ad...
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Fairness GAN
In this paper, we introduce the Fairness GAN, an approach for generating...
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Optimizing Kernel Machines using Deep Learning
Building highly non-linear and non-parametric models is central to sever...
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Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Disentangled representations, where the higher level data generative fac...
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Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Semi-supervised learning methods using Generative Adversarial Networks (...
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A Deep Learning Approach To Multiple Kernel Fusion
Kernel fusion is a popular and effective approach for combining multiple...
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Understanding Innovation to Drive Sustainable Development
Innovation is among the key factors driving a country's economic and soc...
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