On-device research index

arXiv research

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.

169,341 papers · 148 categories

Trend · papers per month

count % of corpus

9 results for “Broadband”

Broadband DOA estimation using Convolutional neural networks trained with noise signalscs.SD

CNN trained with noise signals for broadband DOA estimation.

problem Broadband DOA estimation in noisy environments.

method CNN trained on phase components of noise signals for DOA estimation.

result CNN trained with noise signals generalizes well to speech sources.

CNN DOA estimation Broadband Noise Speech

2017-05-02abs ↗pdf ↗

Encoding Binary Events from Continuous Time Series in Rooted Trees using Contrastive Learningcs.LG

Study infers tree topology from customer data using contrastive learning.

problem Inferring local network topology from customer data.

method Contrastive learning approach for binary event encoding from continuous time series.

result Preliminary results show potential for valuable encoder learning.

network topology customer data contrastive learning binary event encoding rooted trees

2024-01-02abs ↗pdf ↗

Ask less - Scale Market Research without Annoying Your Customerscs.LG

Reduce survey questions to scale market research without annoying customers.

problem Performing market research by surveying customers with many questions is inefficient and annoying.

method Used Bayesian networks to model and reduce the number of questions asked to customers.

result Demonstrated the effectiveness of the approach using an example of broadband customer segmentation.

market research customer surveys Bayesian networks market segmentation customer annoyance

2019-01-25abs ↗pdf ↗

Large Scale Organization and Inference of an Imagery Dataset for Public Safetycs.CV

Researchers organize and analyze a large public safety imagery dataset.

problem Efficiently organizing and analyzing a vast public safety imagery dataset.

method Hierarchical organization approach using Lincoln Laboratory Supercomputing Cluster for compute and storage.

result Successfully organized and evaluated the dataset with large-scale imagery inference across terabytes of data.

public safety imagery dataset hierarchical organization supercomputing cluster image inference

2019-08-16abs ↗pdf ↗

Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satelliteseess.SP

Paper uses DRL for automated power allocation in satellites.

problem Manual resource allocation is impractical for satellites with many power degrees of freedom.

method Continuous state and action spaces, Proximal Policy Optimization (PPO) algorithm.

result DRL shows promising results for minimum Unmet System Demand and power consumption.

Deep Reinforcement Learning Satellite Power Allocation Continuous State Proximal Policy Optimization Unmet System Demand

2019-06-03abs ↗pdf ↗

An ensemble classifier for vibration-based quality monitoringcs.CV

A novel ensemble classifier improves vibration-based quality monitoring accuracy.

problem Developing high accuracy classification methods for general datasets.

method Dempster-Shafer theory of evidence with three remedies for conflicting evidences.

result The proposed ensemble classifier outperforms state-of-the-art fusion techniques.

vibration-based quality monitoring ensemble classifier Dempster-Shafer theory pattern recognition machine learning

2020-07-17abs ↗pdf ↗

Deep learning for universal linear embeddings of nonlinear dynamicsmath.DS

Deep learning discovers intrinsic coordinates for nonlinear dynamics.

problem Identifying coordinate transformations for strongly nonlinear dynamics.

method Modified auto-encoder network to discover Koopman eigenfunctions.

result Compact and efficient embedding of intrinsic rank for nonlinear systems.

deep learning Koopman operator nonlinear dynamics trajectory data intrinsic coordinates

2017-12-27abs ↗pdf ↗

Exposing Hardware Building Blocks to Machine Learning Frameworkscs.LG

The thesis explores how to integrate machine learning with hardware constraints.

problem Designing efficient neural networks for real-time processing with hardware limitations.

method Developed a library for training and converting sparse quantized neural networks to hardware.

result Demonstrated how to design and optimize neural networks for FPGA-based hardware.

Machine Learning Hardware Integration Neural Networks Quantization FPGA

2020-04-10abs ↗pdf ↗

Constraining the recent star formation history of galaxies : an Approximate Bayesian Computation approachastro-ph.GA

Method identifies galaxies with recent star formation variations.

problem Identify galaxies with recent star formation variations.

method Approximate Bayesian Computation (ABC) with machine learning.

result Flexible star formation histories are needed for accurate modeling.

star formation history galaxies star formation rate stellar mass Approximate Bayesian Computation

2020-02-18abs ↗pdf ↗