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Mar 3, 2026 · Artificial Intelligence

How NanoSD Cuts 90% Parameters to Enable Real‑Time Photo Editing on Mobile

NanoSD distills Stable Diffusion 1.5 into a 130 M‑parameter model that runs inference in 20 ms on a Qualcomm SM8750 NPU, using hardware‑aware module pruning, module‑level knowledge distillation, and Bayesian optimization to achieve Pareto‑optimal quality‑efficiency trade‑offs for on‑device image restoration.

Bayesian OptimizationStable Diffusionknowledge distillation

0 likes · 14 min read

How NanoSD Cuts 90% Parameters to Enable Real‑Time Photo Editing on Mobile

Bighead's Algorithm Notes

Dec 21, 2025 · Artificial Intelligence

Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading

Logic-Q introduces a program‑sketch paradigm that injects lightweight, plug‑and‑play market‑trend logic into deep reinforcement learning agents, dramatically improving trend detection, reducing drawdowns, and outperforming state‑of‑the‑art DRL strategies on multiple quantitative‑trading benchmarks.

Bayesian OptimizationLogic-QMarket Trend Detection

0 likes · 12 min read

Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading

Bighead's Algorithm Notes

Oct 10, 2025 · Artificial Intelligence

Quantitative Finance Paper Digest (Sep 27 – Oct 10 2025)

This digest summarizes recent arXiv papers that introduce new AI‑driven methods for portfolio similarity, Bayesian portfolio optimization, end‑to‑end deep‑learning portfolio construction, large‑language‑model‑based financial prediction, and multi‑agent crypto‑trading systems, highlighting their datasets, architectures, and empirical gains.

Bayesian OptimizationDeep Learningasset allocation

0 likes · 18 min read

Quantitative Finance Paper Digest (Sep 27 – Oct 10 2025)

Test Development Learning Exchange

Apr 4, 2024 · Artificial Intelligence

Scikit‑Optimize (skopt): Features, Use Cases, and Code Examples

Scikit‑Optimize is a Python library for black‑box optimization that offers adaptable, efficient algorithms, hyper‑parameter tuning, interactive monitoring, and seamless Scikit‑Learn integration, illustrated with five comprehensive code examples covering basic usage, constrained and interactive optimization, and visualization.

Bayesian OptimizationBlack-Box Optimizationhyperparameter tuning

0 likes · 7 min read

Scikit‑Optimize (skopt): Features, Use Cases, and Code Examples

DataFunSummit

Nov 21, 2023 · Artificial Intelligence

Automatic Hyperparameter Tuning in Tencent Recommendation System (TRS): Techniques, Evolution, and Practice

This article presents an in‑depth overview of Tencent's TRS automatic hyperparameter tuning, covering background, challenges, the evolution from Bayesian optimization to evolution strategies and reinforcement learning, a systematic platform solution, real‑world deployment results, and a Q&A session.

Bayesian OptimizationEvolution StrategiesOnline Learning

0 likes · 20 min read

Automatic Hyperparameter Tuning in Tencent Recommendation System (TRS): Techniques, Evolution, and Practice

WeChat Backend Team

Oct 25, 2023 · Fundamentals

Mastering Metric Covariance for Accurate A/B Test Analysis

This article explains the statistical foundations of A/B testing, introduces potential outcomes and average treatment effect, defines metric covariance, and presents practical estimation methods—including naive, data‑augmentation, and bucket‑based approaches—along with real‑world performance evaluations and applications such as variance reduction and Bayesian optimization.

A/B testingBayesian Optimizationexperimental design

0 likes · 18 min read

Mastering Metric Covariance for Accurate A/B Test Analysis

DataFunTalk

Nov 27, 2022 · Product Management

Challenges of Traditional Experiment Systems and the Vision for Next‑Generation Evaluation Platforms

The article examines why classic A/B testing frameworks struggle with modern internet services—highlighting issues of intervention, measurement, and analysis—while proposing an observational, dynamic, and decision‑oriented next‑generation experiment system that leverages statistical learning and Bayesian optimization.

A/B testingBayesian OptimizationExperiment Platform

0 likes · 11 min read

Challenges of Traditional Experiment Systems and the Vision for Next‑Generation Evaluation Platforms

Code DAO

Jan 15, 2022 · Artificial Intelligence

How Tuun’s Automated Data Augmentation Boosts AI Model Accuracy

The article explains how Tuun, an open‑source Bayesian‑optimization tool, automatically searches data‑augmentation policies for machine‑learning models, details the setup with Microsoft NNI, provides code and configuration examples, and presents experiments on CIFAR‑10/100 and SVHN showing that Tuun‑generated policies match or surpass expert‑tuned strategies and further improve performance when combined.

AutoMLBayesian OptimizationImage Classification

0 likes · 14 min read

How Tuun’s Automated Data Augmentation Boosts AI Model Accuracy

Efficient Ops

Jul 17, 2021 · Databases

How AutoTiKV’s Machine Learning Optimizes Beaver Search Engine Performance

This article describes how the Beaver search engine’s many performance‑related configuration parameters can be automatically tuned using machine‑learning techniques from OtterTune and AutoTiKV, detailing the background research, Gaussian Process regression model, Bayesian optimization process, implementation steps, test results, and future improvements.

Bayesian OptimizationBeaverDatabase Performance

0 likes · 23 min read

How AutoTiKV’s Machine Learning Optimizes Beaver Search Engine Performance

Hulu Beijing

Mar 28, 2019 · Artificial Intelligence

Mastering Bayesian Hyperparameter Optimization: A Practical Guide

This article explains what hyper‑parameters are, why their tuning is a black‑box problem, and how Bayesian optimization—using surrogate models, acquisition functions, and posterior inference—offers a more efficient alternative to grid or random search, while also listing popular open‑source tools and discussing its limitations.

Acquisition FunctionAutoMLBayesian Optimization

0 likes · 8 min read

Mastering Bayesian Hyperparameter Optimization: A Practical Guide