A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
CN normalizes channels for better time series model performance.
Adversarial attacks are ineffective when the surrogate models are trained with different channel effects.
Customers are usually exposed to online digital advertisement channels, such as email marketing, display advertising, paid search engine marketing, along their way to purchase or subscribe products( aka. conversion). The marketers track all the customer journey data and try to measure the effectiveness of each advertis…
PruneNet efficiently prunes channels in deep networks, improving accuracy and performance.
Attention mechanism is a hot spot in deep learning field. Using channel attention model is an effective method for improving the performance of the convolutional neural network. Squeeze-and-Excitation block takes advantage of the channel dependence, selectively emphasizing the important channels and compressing the rel…
New adaptive SGD algorithms for federated learning over physical channels.
CDA framework infers channel influence from aggregated data without user identifiers.
Optical Wireless Communication (OWC) propagation channel characterization plays a key role on the design and performance analysis of Vehicular Visible Light Communication (VVLC) systems. Current OWC channel models based on deterministic and stochastic methods, fail to address mobility induced ambient light, optical tur…
A framework for multi-agent communication over noisy channels in reinforcement learning.
Proposes dynamic channel pruning during neural network training.
Gradual pruning reduces inference cost by pruning least important channels during training.
Normalization layers are widely used in deep neural networks to stabilize training. In this paper, we consider the training of convolutional neural networks with gradient descent on a single training example. This optimization problem arises in recent approaches for solving inverse problems such as the deep image prior…
Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the…
Banking system crises are complex events that in a short span of time can inflict extensive damage to banks themselves and to the external economy. The crisis literature has so far identified a number of distinct effects or channels that can propagate distress contagiously both directly within the banking network itsel…
We consider a wireless communication system that consists of a transmitter, a receiver, and an adversary. The transmitter transmits signals with different modulation types, while the receiver classifies its received signals to modulation types using a deep learning-based classifier. In the meantime, the adversary makes…
To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning. Precisely, we argue that channels reveali…
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…
A new method prunes neural network channels based on operation characteristics.
We study channel number reduction in combination with weight binarization (1-bit weight precision) to trim a convolutional neural network for a keyword spotting (classification) task. We adopt a group-wise splitting method based on the group Lasso penalty to achieve over 50% channel sparsity while maintaining the netwo…
Random orthogonalization improves FL in massive MIMO systems without CSI.
A method uses CG to create efficient channels for ideal observers.
Proposes GPCA module for channel attention in CNNs using Gaussian processes.
Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessment of these disorders, based on voice signals. However, these signals can be sensitive to the recording devices. Indeed, the channel effect …
ReQuestNet simplifies 5G channel estimation with a unified model.
DUET enhances multivariate time series forecasting by clustering time and channels.
This paper examines how the U.S.--China trade war affects stock markets, finding evidence of financial contagion and changes in risk channels.
Enhances Transformer models for multivariate time series with dataset-specific channel masks.
Proposes HOTA-FedGradNorm for faster and robust PFL in noisy channels.
In this paper we study speaker linking (a.k.a.\ partitioning) given constraints of the distribution of speaker identities over speech recordings. Specifically, we show that the intractable partitioning problem becomes tractable when the constraints pre-partition the data in smaller cliques with non-overlapping speakers…
A-FADMM improves FL scalability and privacy via wireless channel perturbations and interference.
Quantum CNNs improve on multi-channel data processing.
In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated. Therefore, the full data could follow the forest structure and we call this property as \emph{forest sparsity}. It exploi…
A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the …
Paper defends deep learning classifiers against channel-aware adversarial attacks.
Paper proposes neural network for efficient MIMO channel estimation and pilot reduction.
We investigate connections between information-theoretic and estimation-theoretic quantities in vector Poisson channel models. In particular, we generalize the gradient of mutual information with respect to key system parameters from the scalar to the vector Poisson channel model. We also propose, as another contributi…
Geopolitical and geoeconomic shocks affect sovereign risk differently, with distinct transmission channels.
Adversaries with multiple antennas can fool deep learning modulators more effectively.
Sentinel improves time series forecasting by modeling both temporal and channel dependencies.
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…
Optimizes CNNs by directing gradients along output channels.
New algorithms for hypothesis testing in high-dimensional data are shown to be effective under various noisy conditions.
Recent successes and advances in Deep Neural Networks (DNN) in machine vision and Natural Language Processing (NLP) have motivated their use in traditional signal processing and communications systems. In this paper, we present results of such applications to the problem of automatic modulation recognition. Variations …
Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling. While Recurrent Neural Networks (RNNs) have led to significant advances in automated diagnosis …
Bayesian model improves BCI performance for ALS users.
Tiled Squeeze-and-Excite improves channel attention with local spatial context.
Theory proposes neural networks can be initialized for optimal information transmission.
Decomposes spillover effects under misspecified exposure mappings.