arXiv research

Machine learning approximates phase transitions using Fisher information.

Complex-valued neural networks improve seismic data analysis by preserving phase information.

Paper improves phase identification in power systems using information theory.

The paper studies phase transitions in Information Bottleneck for representation learning.

Sharp asymptotics derived for phase retrieval and compressed sensing with random generative priors.

The paper applies Information Bottleneck theory to CNNs and finds compression phase not always present.

Study shows reverberant phase is not essential for weakly-supervised dereverberation.

Improved regret bounds for bandit phase retrieval.

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable …

New method extracts hidden phases in binary mixtures using tubular tilings.

Paper tackles joint community detection and phase synchronization in stochastic block models.

Paper analyzes latent space geometry in generative models using Fisher information.

To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utilized to assess active usage of the content platform. If there is active use, the appearance information is utilized in Phase 2 to infer behavi…

Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer. The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen. The exact…

An algorithmically hard phase was described in a range of inference problems: even if the signal can be reconstructed with a small error from an information theoretic point of view, known algorithms fail unless the noise-to-signal ratio is sufficiently small. This hard phase is typically understood as a metastable bran…

This study analyzes VAEs using ID and II, revealing a transition in behaviour and distinct training phases.

The paper analyzes phase transitions in transfer learning for perceptrons.

This paper shows how learning the phase-amplitude coupling improves bio-signal classification.

Motivated by the need for accurate frequency information, a novel algorithm for estimating the fundamental frequency and its rate of change in three-phase power systems is developed. This is achieved through two stages of Kalman filtering. In the first stage a quaternion extended Kalman filter, which provides a unified…

Persistent entropy detects phase transitions in complex systems.

Improved statistical inference for expensive data using machine learning predictions.

Neural networks learn simpler features first, then more complex ones; Fourier analysis reveals this pattern.

Near-optimal sample complexity for phase retrieval with generative priors.

A new model of V1 using orientation, frequency, and phase.

In this paper, we address the problem of reconstructing a time-domain signal (or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude spectrograms do not contain phase information, we must restore or infer phase information to reconstruct a time-domain signal. One widely used approach for dealing w…

We study graph matching with correlated Gaussian features and find thresholds for exact recovery.

Recently, the principal component pursuit has received increasing attention in signal processing research ranging from source separation to video surveillance. So far, all existing formulations are real-valued and lack the concept of phase, which is inherent in inputs such as complex spectrograms or color images. Thus,…

We study the market impact of a meta-order in the framework of the Minority Game. This amounts to studying the response of the market when introducing a trader who buys or sells a fixed amount h for a finite time T. This perturbation introduces statistical arbitrages that traders exploit by adapting their trading strat…

Proposes QNN to protect input privacy in neural networks.

Algorithm finds frequencies, amplitudes, and phases of sinusoids in noisy data.

Review of information plane analyses in neural networks, highlighting mixed results and methodological challenges.

Method detects phase transitions in financial markets using eigenvalue decomposition.

A new image completion method inspired by brain cells.

Paper tackles sparse phase retrieval with a novel Bayesian approach.

Framework for multi-scale clustering using phase transitions.

New method uses image registration to recover complex signals from amplitude data.

We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients ($n \gg p \gg 1$). In this regime, optimization algorithms can immensely benefit from approximate second order information.…

Study on detecting hierarchical community structures in networks.

Enhances neural network dynamics to boost computational capacity.

Belief Propagation outperforms other algorithms in reconstructing binary symmetric channel trees.

Paper eliminates warm-up phase for PO in linear MDPs, achieving optimal regret.

Paper tackles phase retrieval with robust gradient descent for noisy data.

In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning tech…

New learning rate approach reveals phase transitions in SGD performance.

Early training phase affects deep neural network optimization and generalization.

Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. More than four decades after it was first proposed, the seminal error reduction algorithm of (Gerchberg and Saxton 1972) and (Fienup 1982) is still the popular choice for solving many variants o…

The support recovery problem consists of determining a sparse subset of variables that is relevant in generating a set of observations. In this paper, we study the support recovery problem in the phase retrieval model consisting of noisy phaseless measurements, which arises in a diverse range of settings such as optica…

In this paper, we propose a phase shift deep neural network (PhaseDNN) which provides a wideband convergence in approximating a high dimensional function during its training of the network. The PhaseDNN utilizes the fact that many DNN achieves convergence in the low frequency range first, thus, a series of moderately-s…