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Found 6 papers, 2 papers with code
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions
Empirical observation of high dimensional phenomena, such as the double descent behaviour, has attracted a lot of interest in understanding classical techniques such as kernel methods, and their implications to explain generalization properties of neural networks.
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations
Improving generalization is a major challenge in audio classification due to labeled data scarcity.
Implicit Bias of Linear RNNs
The degree of this bias depends on the variance of the transition kernel matrix at initialization and is related to the classic exploding and vanishing gradients problem.
Low-Rank Nonlinear Decoding of $μ$-ECoG from the Primary Auditory Cortex
This decoding problem is particularly challenging due to the complexity of neural responses in the auditory cortex and the presence of confounding signals in awake animals.
Generalization Error of Generalized Linear Models in High Dimensions
We provide a general framework to characterize the asymptotic generalization error for single-layer neural networks (i. e., generalized linear models) with arbitrary non-linearities, making it applicable to regression as well as classification problems.
Input-Output Equivalence of Unitary and Contractive RNNs
Unitary recurrent neural networks (URNNs) have been proposed as a method to overcome the vanishing and exploding gradient problem in modeling data with long-term dependencies.
