Research
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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.

168,932 papers · 148 categories

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1122 · Feb 201519922001200920172026
29 results for browser

Percival uses deep learning to block ads in real-time, minimizing performance impact.

problem Real-time ad blocking in browsers with minimal performance overhead.
method Deep learning for image classification integrated into browser's rendering pipeline.
result Percival achieves 96.76% accuracy in blocking ads, comparable to block lists.

With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticate…

2014-12-08abs ↗pdf ↗

Semi-supervised model removes noisy content from webpages.

problem Extracting relevant content from webpages with ads and noise.
method Graph representation of webpage, semi-supervised learning with Gaussian Random Fields.
result Preliminary results show successful extraction of relevant content.

Production recommendation systems rely on embedding methods to represent various features. An impeding challenge in practice is that the large embedding matrix incurs substantial memory footprint in serving as the number of features grows over time. We propose a similarity-aware embedding matrix compression method call…

2019-02-26abs ↗pdf ↗

Bäcklund transformations for smooth and ``space discrete'' Hashimoto surfaces are discussed and a geometric interpretation is given. It is shown that the complex curvature of a discrete space curve evolves with the discrete nonlinear Schrödinger equation (NLSE) of Ablowitz and Ladik, when the curve evolves with the Has…

2000-07-25abs ↗pdf ↗

A new model learns preferences incrementally without personal data.

problem Incremental session-based recommendation without personal data.
method Memory Augmented Neural model (MAN) that combines a neural recommender with a nonparametric memory.
result MAN consistently outperforms existing methods in incremental session-based recommendation.

Estimates conversion probabilities from click sequences with privacy constraints.

problem Training models in advertising with limited direct click-conversion links.
method Formalizes learning from attribution sets, constructs unbiased estimator, applies Empirical Risk Minimization.
result Empirical Risk Minimization achieves generalization guarantees and robustness against prior errors.

The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high-dimensional data. tSNE reveals clusters of high-dimensional data points at different scales while it requires only minimal tuning of its pa…

2018-05-28abs ↗pdf ↗

CoinTossX is a low-latency, open-source matching engine for financial trading.

problem Efficiently matching orders in financial markets with low latency and high throughput.
method Developed in Java, orders submitted via UDP SBE, low-latency message transport (Aeron Media Driver). Separates order generation and matching.
result Demonstrated low-latency, high-throughput performance in various deployment scenarios.

This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoni…

2015-02-02abs ↗pdf ↗

SecVM preserves user privacy in training SVMs for classification tasks.

problem Training supervised classifiers on sensitive user data while maintaining privacy.
method A novel secret vector machine (SecVM) framework for training linear SVMs in a distributed, privacy-preserving manner.
result SecVM outperforms baselines in a large-scale online evaluation, preserving user privacy and classification accuracy.

Report on enhancing trust in ML models with visualizations.

problem Understanding and trusting the results of black box ML models.
method Categorization of visualization techniques, statistical overview, topic analyses, and interactive web-based survey.
result Expanded categorization of trust against different facets of interactive ML.