We propose two stochastic gradient MCMC methods for sampling from Bayesian posterior distributions defined on Riemann manifolds with a known geodesic flow, e.g.

One of the fundamental cognitive abilities of humans is to quickly resolve uncertainty by generating hypotheses and testing them via active trials.

Deep Compression: Compressing Deep Neural Networkswith Trained Quantizationand Huffman Coding.

Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.

Let θ and φ denote the optimal parameters after model training. The detailed statistics of three datasets used in this paper are listed in Table 1. In this paper, when evaluating the performance in the standard experimental scenario and in the label-scarce scenario, we compare with six state-of-the-art baselines used for graph-based semisupervised learning. Three of them are deterministic GNN-based models, which are GCN [1], Graph Attention Networks(GAT)[2]andGraphSAGE[3]respectively.

The resolution and quality of images produced by generative adversarial networks (GAN) [19] have seen rapid improvement recently [27, 11, 29, 30].

He Xiaopeng launches Xpeng's next-gen Iron humanoid robot during a press conference at the company's headquarters in Guangzhou on November 5, 2025. He Xiaopeng launches Xpeng's next-gen Iron humanoid robot during a press conference at the company's headquarters in Guangzhou on November 5, 2025. It was a controversy laced with pride for He Xiaopeng. In November, He, the founder and CEO of Chinese physical AI firm XPeng, had just debuted his new humanoid robot, IRON, whose balance, posture shifts, and coquettish swagger mirrored human motion with such eerie precision that a slew of netizens accused him of faking the demonstration by putting a human in a bodysuit. To silence the naysayers, He boldly cut open the robot's leg live on stage to reveal the intricate mechanical systems that allow it to adapt to uneven surfaces and maintain stability just like the human body. "At first, it made me sad," He tells TIME in his Guangzhou headquarters.

GlobalBuildingAtlas includes almost every habitable structure on Earth. Breakthroughs, discoveries, and DIY tips sent every weekday. Researchers in Germany recently accomplished a truly audacious feat of cartography . Using a diverse array of datasets, a team at the Technical University of Munich released GlobalBuildingAtlas, the first high-resolution mapping model featuring every structure in the world at a given point in time. With over 2.75 billion buildings detailed in the map, the endeavor will help create accurate analyses of urban structures, volume calculations, and infrastructure planning around the planet.