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.

168,742 papers · 148 categories

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6 results for “auto-labeling”

FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processorscs.LG

FLAME auto-labels mobile data efficiently on diverse processors.

problem Accurately and efficiently labeling mobile data with unknown labels on heterogeneous processors.

method Self-adaptive auto-labeling system Flame that schedules and executes workloads on mobile processors.

result Flame achieves high labeling accuracy and performance on heterogeneous mobile processors.

mobile data labeling heterogeneous processors auto-labeling unknown labels runtime system

2020-03-03abs ↗pdf ↗

Pearls from Pebbles: Improved Confidence Functions for Auto-labelingcs.LG

New method improves auto-labeling accuracy by optimizing confidence functions.

problem Overconfident model scores lead to poor TBAL performance.

method Developed a new post-hoc method, Colander, to optimize TBAL confidence functions.

result Achieves up to 60% improvement in coverage over baseline methods.

auto-labeling confidence functions TBAL calibration model performance

2024-04-24abs ↗pdf ↗

Promises and Pitfalls of Threshold-based Auto-labelingcs.LG

TBAL reduces manual annotation but requires validated data.

problem Creating large, high-quality labeled datasets.

method Threshold-based auto-labeling using human validation data.

result Sample complexity bounds on validation data needed.

auto-labeling threshold-based machine learning validation data sample complexity

2022-11-22abs ↗pdf ↗

MCAL: Minimum Cost Human-Machine Active Labelingcs.LG

MCAL reduces labeling costs by 6x for auto-labeling data sets.

problem Expensive human annotation for ground-truth data sets.

method Iterative approach that trains a classifier to auto-label part of the data set, determining which samples to label using humans and which to label using the classifier at each step.

result 6x lower overall cost compared to human labeling the entire data set, always cheaper than competing strategies.

human-machine labeling ground-truth generation classifier training cost minimization active learning

2020-06-24abs ↗pdf ↗

Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networksstat.ML

Graph neural network predicts vehicle interactions and trajectories for autonomous driving.

problem Predicting future motion of vehicles in traffic scenes.

method Graph neural network that jointly predicts interaction modes and 5-second future trajectories.

result Jointly predicting trajectories and interaction modes leads to lower trajectory error.

autonomous driving graph neural networks trajectory prediction interaction prediction traffic scene

2019-12-17abs ↗pdf ↗

Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Datacs.LG

With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form. In this paper, we develop a deep learning-based method, called Drive2Vec, for embedding such s…

2018-06-12abs ↗pdf ↗