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08/07/2023
Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
Clinically deployed segmentation models are known to fail on data outsid...
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07/14/2023
A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks
A key property of neural networks (both biological and artificial) is ho...
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02/07/2023
Towards causally linking architectural parametrizations to algorithmic bias in neural networks
Training dataset biases are by far the most scrutinized factors when exp...
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10/07/2022
Evaluating the Performance of StyleGAN2-ADA on Medical Images
Although generative adversarial networks (GANs) have shown promise in me...
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09/08/2022
Dyadic Interaction Assessment from Free-living Audio for Depression Severity Assessment
Psychomotor retardation in depression has been associated with speech ti...
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03/16/2022
Understanding robustness and generalization of artificial neural networks through Fourier masks
Despite the enormous success of artificial neural networks (ANNs) in man...
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10/02/2020
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Humans have an inherent ability to learn novel concepts from only a few ...
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06/12/2020
LaRVAE: Label Replacement VAE for Semi-Supervised Disentanglement Learning
Learning interpretable and disentangled representations is a crucial yet...
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03/06/2020
Semi-Supervised StyleGAN for Disentanglement Learning
Disentanglement learning is crucial for obtaining disentangled represent...
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02/21/2020
Adversarial Attacks on Machine Learning Systems for High-Frequency Trading
Algorithmic trading systems are often completely automated, and deep lea...
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02/27/2019
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
We investigate the internal representations that a recurrent neural netw...
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12/06/2016
Semi-Supervised Learning with the Deep Rendering Mixture Model
Semi-supervised learning algorithms reduce the high cost of acquiring la...
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12/06/2016
A Probabilistic Framework for Deep Learning
We develop a probabilistic framework for deep learning based on the Deep...
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08/17/2015
A Deep Learning Approach to Structured Signal Recovery
In this paper, we develop a new framework for sensing and recovering str...
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04/02/2015