arXiv research

A Python package solves source duplication in single channel LVMs using spectral regularisation.

In this article we give our contribution to the problem of segmentation with plug-in procedures. We give general sufficient conditions under which plug in procedure are efficient. We also give an algorithm that satisfy these conditions. We give an application of the used algorithm to hyperspectral images segmentation. …

This work proposes a novel autoencoder for fusing visible and infrared images.

Quantum computers can enhance spectral methods in machine learning.

The kernel null-space technique and its regression-based formulation (called one-class kernel spectral regression, a.k.a. OC-KSR) is known to be an effective and computationally attractive one-class classification framework. Despite its outstanding performance, the applicability of kernel null-space method is limited d…

The canonical trace and the Wodzicki residue on classical pseudodifferential operators on a closed manifold are characterised by their locality and shown to be preserved under lifting to the universal covering as a result of their local feature. As a consequence, we lift a class of spectral $ζ$-invariants using lifted …

New method calculates eta invariant without analytic continuation.

Develops a new approach to spectral asymmetry using microlocal analysis.

New spectral invariants distinguish Joyce orbifolds from other manifolds.

New defence against data-poisoning attacks in neural networks.

The report analyzes infinite-dimensional output space regression.

This paper improves inverse problem solving with weakly convex regularisers and proves convergence.

Spectral portfolio theory links neural networks to wealth dynamics via SGD weight matrices.

GNIs induce a regulariser that penalizes high-frequency components in neural network activations.

Generalised regularisation equals robustness for exotic function classes.

We consider a general regularised interpolation problem for learning a parameter vector from data. The well known representer theorem says that under certain conditions on the regulariser there exists a solution in the linear span of the data points. This is the core of kernel methods in machine learning as it makes th…

We introduce a class of regularisable infinite dimensional principal fibre bundles which includes fibre bundles arising in gauge field theories like Yang-Mills and string theory and which generalise finite dimensional Riemannian principal fibre bundles induced by an isometric action. We show that the orbits of regulari…

New framework monitors neural network training and reveals regularisation mechanisms.

This work uncovers algorithm-dependent regularisation in diffusion models.

Despite recent advances in regularisation theory, the issue of parameter selection still remains a challenge for most applications. In a recent work the framework of statistical learning was used to approximate the optimal Tikhonov regularisation parameter from noisy data. In this work, we improve their results and ext…

Spectral decoupling improves neural network generalization in medical imaging.

Generalisation of a deep neural network (DNN) is one major concern when employing the deep learning approach for solving practical problems. In this paper we propose a new technique, named approximated orthonormal normalisation (AON), to improve the generalisation capacity of a DNN model. Considering a weight matrix W …

New method prevents deep learning forgetting past by remembering key examples.

Novel approach finds implicit regularisation in two-player games using BEA.

Regularization preserves topological data structure in autoencoders.

Regularisation improves ML classifier stability against poisoning attacks.

Investigates gradient descent dynamics and introduces new regularisation methods.

Researchers compare different gradient methods for ridge regression, finding conjugate gradients have similar performance.

Extends shapelet transform to irregular time series, improving interpretability and efficiency.

Study on GD and SGD over diagonal networks, focusing on stepsizes and regularisation.

We propose graph-dependent implicit regularisation strategies for distributed stochastic subgradient descent (Distributed SGD) for convex problems in multi-agent learning. Under the standard assumptions of convexity, Lipschitz continuity, and smoothness, we establish statistical learning rates that retain, up to logari…

This work shows how penalising bias terms in norm regularisation leads to sparse solutions.

Regularizes ML algorithms for robust multivariate analysis against distribution shifts.

Study on how noise and variation-norm regularisation help shallow ReLU networks use fewer neurons.

This work optimizes RL algorithms using entropy regularisation for continuous-time LQ problems.

Paper studies particle method for LSV model calibration, proving convergence and error bounds.

Effective regularisation of neural networks is essential to combat overfitting due to the large number of parameters involved. We present an empirical analogue to the Lipschitz constant of a feed-forward neural network, which we refer to as the maximum gain. We hypothesise that constraining the gain of a network will h…

New insights into how neural networks learn features, especially when they are very wide.

Study on neural networks with regularisation and its impact on training dynamics.

Recent work has established the equivalence between deep neural networks and Gaussian processes (GPs), resulting in so-called neural network Gaussian processes (NNGPs). The behaviour of these models depends on the initialisation of the corresponding network. In this work, we consider the impact of noise regularisation …

We show that many standard results of Lorentzian causality theory remain valid if the regularity of the metric is reduced to $C^{1,1}$. Our approach is based on regularisations of the metric adapted to the causal structure.

Regularising for invariance to data augmentation improves machine learning models.

Unified understanding of three continual learning regularisation methods.

New ADMM method for PARAFAC2 tensor decomposition with flexible regularization.

The paper studies $m$-positive currents and line bundles on complex manifolds.

We develop a new method for regularising neural networks. We learn a probability distribution over the activations of all layers of the model and then insert imputed values into the network during training. We obtain a posterior for an arbitrary subset of activations conditioned on the remainder. This is a generalisati…

SPReD uses uncertainty to decide imitation from demonstrations.

Novel method decorrelates neurons for better deep learning model generalization.