arXiv research

A new DVAE architecture improves channel estimation by incorporating temporal correlations.

A method for self-supervised learning of multivariate time series data across varying channels.

This study shows how monetary uncertainty affects stock market reactions to macroeconomic news.

FPGA-based multi-layer equalizer adapts to changing channels.

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmit…

CN normalizes channels for better time series model performance.

Bayesian model improves BCI performance for ALS users.

Novel NDL framework improves spike detection accuracy and channel localization in EEG/MEG data.

New algorithm reduces age of information in wireless networks with unknown channel reliability.

To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. We employ the proposed framework …

We propose doubly nested network(DNNet) where all neurons represent their own sub-models that solve the same task. Every sub-model is nested both layer-wise and channel-wise. While nesting sub-models layer-wise is straight-forward with deep-supervision as proposed in \cite{xie2015holistically}, channel-wise nesting has…

A-FADMM improves FL scalability and privacy via wireless channel perturbations and interference.

SepIt improves speech separation for multiple speakers.

Ultrasound (US) imaging is based on the time-reversal principle, in which individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented as a delay-and-sum (DAS) beamformer, the image quality quickly degrades as the…

Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI). ViterbiNet is obtained by integrating deep neural networks (DNNs) into the Viterbi algorithm. We identif…

New techniques improve channel prediction in noisy wireless systems.

We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions b…

Study quantifies systemic risk in DeFi using network analysis.

ReQuestNet simplifies 5G channel estimation with a unified model.

Unified Bayesian framework for LTV system identification using neural networks and Gaussian Processes.

New algorithm reduces communication time in federated learning.

Study neural communication systems with bandwidth-limited channels.

Resource allocation improved using machine learning from terminal positions.

DUET enhances multivariate time series forecasting by clustering time and channels.

SOR-Mamba improves Mamba for robust time series forecasting by minimizing channel order bias.

In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant…

In this study we present a kernel based convolution model to characterize neural responses to natural sounds by decoding their time-varying acoustic features. The model allows to decode natural sounds from high-dimensional neural recordings, such as magnetoencephalography (MEG), that track timing and location of human …

The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we face both challe…

New framework improves multivariate time series forecasting by minimizing redundant information.

A new approach corrects bias in federated learning due to varying communication links.

Enhances Transformer models for multivariate time series with dataset-specific channel masks.

This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channel gating identifies regions in the features that contribute less to the classification result, and skips the computation on a subset of the …

Balanced Neural ODEs combine VAEs and Neural ODEs for efficient time series modeling.

Sentinel improves time series forecasting by modeling both temporal and channel dependencies.

AverageTime uses simple averaging to enhance long-term time series forecasting.

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenge…

Deep learning (DL) based autoencoder has shown great potential to significantly enhance the physical layer performance. In this paper, we present a DL based autoencoder for interference channel. Based on a characterization of a k-user Gaussian interference channel, where the interferences are classified as different le…

A deep-learning-based surrogate model is developed and applied for predicting dynamic subsurface flow in channelized geological models. The surrogate model is based on deep convolutional and recurrent neural network architectures, specifically a residual U-Net and a convolutional long short term memory recurrent networ…

New exact tests detect changepoints in binary and count data, especially when normal approximations fail.

Proposes dynamic channel pruning during neural network training.

IETNet identifies important channels for MVTS classification.

Improved Granger causality method for dynamic time series data.

Study online learning of quantum processes, showing feasibility for certain types.

In this paper, we present a deep learning (DL) algorithm for channel estimation in communication systems. We consider the time-frequency response of a fast fading communication channel as a two-dimensional image. The aim is to find the unknown values of the channel response using some known values at the pilot location…

Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied quantitatively---could yield important insights into the nature and intrinsic dynamics of seizures. A…

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

Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.

Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging. We study learning algorithms over the unknown reward distributions and prove a sub-linear, $O(\sqrt{T}\log T)$, regret bound for a variant of Thompson sampling. Our…