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09/15/2023
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
Systematic generalization is a crucial aspect of intelligence, which ref...
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06/15/2023
Modularity Trumps Invariance for Compositional Robustness
By default neural networks are not robust to changes in data distributio...
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03/17/2023
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios....
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01/27/2022
Transformer Module Networks for Systematic Generalization in Visual Question Answering
Transformer-based models achieve great performance on Visual Question An...
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01/25/2022
Do Neural Networks for Segmentation Understand Insideness?
The insideness problem is an aspect of image segmentation that consists ...
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12/17/2021
A Robust Optimization Approach to Deep Learning
Many state-of-the-art adversarial training methods leverage upper bounds...
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12/08/2021
Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connections
Symmetry is omnipresent in nature and perceived by the visual system of ...
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10/30/2021
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations: late-stopping, tuning batch normalization and invariance los
The training data distribution is often biased towards objects in certai...
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07/13/2021
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions
Datasets often contain input dimensions that are unnecessary to predict ...
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06/30/2021
Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers
Neural networks are susceptible to small transformations including 2D ro...
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06/15/2021
How Modular Should Neural Module Networks Be for Systematic Generalization?
Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via...
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07/15/2020
On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations
Recognizing an object's category and pose lies at the heart of visual un...
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06/30/2020
Is Robustness To Transformations Driven by Invariant Neural Representations?
Deep Convolutional Neural Networks (DCNNs) have demonstrated impressive ...
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12/10/2019
Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks
Deep neural networks (DNNs) perform well on a variety of tasks despite t...
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02/08/2019
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images
The human ability to recognize objects is impaired when the object is no...
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06/29/2018
Theory IIIb: Generalization in Deep Networks
A main puzzle of deep neural networks (DNNs) revolves around the apparen...
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12/30/2017
Theory of Deep Learning III: explaining the non-overfitting puzzle
A main puzzle of deep networks revolves around the absence of overfittin...
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11/14/2016
Herding Generalizes Diverse M -Best Solutions
We show that the algorithm to extract diverse M -solutions from a Condit...
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08/29/2014
Comment on "Ensemble Projection for Semi-supervised Image Classification"
In a series of papers by Dai and colleagues [1,2], a feature map (or ker...
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09/16/2013
SEEDS: Superpixels Extracted via Energy-Driven Sampling
Superpixel algorithms aim to over-segment the image by grouping pixels t...
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07/19/2013