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7 papers · 1 filter
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation
Hongxiao Wang, Hao Zheng, Jianxu Chen +3
Automatic histopathology image segmentation is crucial to disease analysis. Limited available labeled data hinders the generalizability of trained models under the fully supervised…
Mitigating Bias in Calibration Error Estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens +1
For an AI system to be reliable, the confidence it expresses in its decisions must match its accuracy. To assess the degree of match, examples are typically binned by confidence an…
Recurrent neural circuits for contour detection
Drew Linsley, Junkyung Kim, Alekh Ashok +1
We introduce a deep recurrent neural network architecture that approximates visual cortical circuits. We show that this architecture, which we refer to as the gamma-net, learns to…
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks
Thomas Fel, David Vigouroux, Rémi Cadène +1
A plethora of methods have been proposed to explain how deep neural networks reach their decisions but comparatively, little effort has been made to ensure that the explanations pr…
Mixture Representation Learning with Coupled Autoencoders
Yeganeh M. Marghi, Rohan Gala, Uygar Sümbül
Jointly identifying a mixture of discrete and continuous factors of variability without supervision is a key problem in unraveling complex phenomena. Variational inference has emer…
Network Dynamics Governed by Lyapunov Functions: From Memory to Classification
Merav Stern, Eric Shea-Brown
In 1982 John Hopfield published a neural network model for memory retrieval, a model that became a cornerstone in theoretical neuroscience. A key ingredient of the Hopfield model w…