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Found 10 papers, 2 papers with code
Reverse Engineering Self-Supervised Learning
Self-supervised learning (SSL) is a powerful tool in machine learning, but understanding the learned representations and their underlying mechanisms remains a challenge.
Numerical Methods For PDEs Over Manifolds Using Spectral Physics Informed Neural Networks
We provide proofs of our method for the heat equation on the interval and examples of unique network architectures that are adapted to nonlinear equations on the sphere and the torus.
Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions
Multiplication layers are a key component in various influential neural network modules, including self-attention and hypernetwork layers.
PR-DAD: Phase Retrieval Using Deep Auto-Decoders
Phase retrieval is a well known ill-posed inverse problem where one tries to recover images given only the magnitude values of their Fourier transform as input.
Nearest Class-Center Simplification through Intermediate Layers
Recent advances in theoretical Deep Learning have introduced geometric properties that occur during training, past the Interpolation Threshold -- where the training error reaches zero.
Sparsity-Probe: Analysis tool for Deep Learning Models
We propose a probe for the analysis of deep learning architectures that is based on machine learning and approximation theoretical principles.
Wavelet Decomposition of Gradient Boosting
In this paper we introduce a significant improvement to the popular tree-based Stochastic Gradient Boosting algorithm using a wavelet decomposition of the trees.
Function space analysis of deep learning representation layers
In this paper we propose a function space approach to Representation Learning and the analysis of the representation layers in deep learning architectures.
Machine olfaction using time scattering of sensor multiresolution graphs
In this paper we construct a learning architecture for high dimensional time series sampled by sensor arrangements.
Adaptive Compressed Tomography Sensing
One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image.
