Learning Adaptive Gaussian Sampling for 3D Generation: Density‑Sampled Gaussians (DeG) at SIGGRAPH 2026

The SIGGRAPH 2026 paper “Generative 3D Gaussians with Learned Density Control” introduces Density‑Sampled Gaussians (DeG), a differentiable framework that lets a model learn where to place Gaussian splats by sampling from a learned spatial density, enabling arbitrary‑budget, non‑uniform 3D representations with higher quality per cost.

3D Gaussian SplattingAdaptive SamplingDifferentiable Rendering

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Learning Adaptive Gaussian Sampling for 3D Generation: Density‑Sampled Gaussians (DeG) at SIGGRAPH 2026

Woodpecker Software Testing

May 8, 2026 · Artificial Intelligence

Beyond More Hardware: In‑Depth Strategies to Accelerate AI Safety Testing

The article dissects AI safety testing bottlenecks and presents four optimization dimensions—testing paradigm, data generation, execution architecture, and feedback loop—offering concrete techniques such as risk‑aware input filtering, gradient‑cache reuse, heterogeneous parallelism, and adaptive sampling that together cut testing time by several folds.

AI safety testingAdaptive SamplingPerformance Optimization

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Beyond More Hardware: In‑Depth Strategies to Accelerate AI Safety Testing

ITFLY8 Architecture Home

Jul 14, 2021 · Backend Development

Adaptive Sampling: Dynamically Tuning Tracing to Cut Overhead

This article explains how adaptive sampling reduces the performance and storage impact of full‑link tracing by dynamically adjusting the sampling rate based on application QPS, outlines the mathematical model, discusses implementation details such as warm‑up, QPS lag, concurrency, and provides a Java reference sampler.

Adaptive SamplingQPSTracing

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Adaptive Sampling: Dynamically Tuning Tracing to Cut Overhead