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Found 8 papers, 1 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.
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.
On the Implicit Bias Towards Minimal Depth of Deep Neural Networks
Finally, we empirically show that the effective depth of a trained neural network monotonically increases when increasing the number of random labels in data.
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.
Deep Learning Solution of the Eigenvalue Problem for Differential Operators
Solving the eigenvalue problem for differential operators is a common problem in many scientific fields.
Certainty Pooling for Multiple Instance Learning
The bag level class prediction is derived from the multiple instances through application of a permutation invariant pooling operator on instance predictions or embeddings.
Solving the functional Eigen-Problem using Neural Networks
In this work, we explore the ability of NN (Neural Networks) to serve as a tool for finding eigen-pairs of ordinary differential equations.
