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.

169,051 papers · 148 categories

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5 results for “Dead-Reckoning”

AI-IMU Dead-Reckoningcs.RO

Novel AI-IMU method accurately estimates vehicle position and orientation.

problem Accurate dead-reckoning for wheeled vehicles using only IMU.

method Kalman filter and deep neural networks for noise adaptation.

result Average 1.10% translational error, competitive with LiDAR or stereo vision methods.

AI-IMU Dead-Reckoning Kalman Filter Inertial Measurement Unit Deep Neural Networks

2019-04-12abs ↗pdf ↗

Dead Reckoning: Counting Your Customers Who Never Say Goodbyeq-fin.GN

Firms miscount their customers who stop buying without saying goodbye.

problem Counting non-contractual customers accurately.

method Estimating repeat purchase probabilities and extrapolating to infinite time.

result The count of alive customers is only partially identified, with a wide range of estimates.

customer churn non-contractual commerce repeat purchase probabilities infinite horizon finite horizon estimates

2026-07-21abs ↗pdf ↗

Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimationcs.RO

Paper proposes a deep learning method for better IMU gyroscope data.

problem Improving accuracy of IMU gyroscope data for robot orientation estimation.

method Dilated convolution neural network, proper loss function, key points identification.

result Algorithm outperforms state-of-the-art on unseen test sequences.

IMU Gyroscopes Deep Learning Attitude Estimation Robot Orientation Dilated Convolutions

2020-02-25abs ↗pdf ↗

Pedestrian Tracking with Gated Recurrent Units and Attention Mechanismscs.CV

A deep learning model improves pedestrian tracking accuracy.

problem Pedestrian tracking accuracy is low, especially with inertial measurement unit.

method Deep learning model using IMU and LIDAR data, attention mechanism.

result Preliminary results show improved accuracy.

pedestrian tracking deep learning recurrent units attention mechanism IMU

2020-05-31abs ↗pdf ↗

Target-agnostic Source-free Domain Adaptation for Regression Taskscs.LG

TASFAR adapts regression models without labeled source data.

problem Lack of labeled source data for domain adaptation.

method Uses prediction confidence to estimate target label distribution and calibrate source model.

result Substantially reduces errors in various regression tasks.

domain adaptation source-free regression prediction confidence label density map

2023-12-01abs ↗pdf ↗