Are Sparse Representations Really Relevant for Image Classification?

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Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

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Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

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Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

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We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

Are Sparse Representations Really Relevant for Image Classification?

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Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

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Improving the accuracy and interpretability of neural networks for wind power forecasting

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Post-correction of Historical Text Transcripts with Large Language Models: An Exploratory Study

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

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Shuffled multi-channel sparse signal recovery

Sparse Autoencoders for Speech Modeling and Recognition

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Enabling Uncertainty Estimation in Iterative Neural Networks

Regularization of polynomial networks for image recognition

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Unlabeled Principal Component Analysis and Matrix Completion