Recent methods for learning a linear subspace from data corrupted by outliers are based on convex L1 and nuclear norm optimization and require the dimension of the subspace and the number of outliers to be sufficiently small [27]. In sharp contrast, the recently proposed Dual Principal Component Pursuit (DPCP) method [22] can provably handle subspaces of high dimension by solving a non-convex L1 optimization problem on the sphere. However, its geometric analysis is based on quantities that are difficult to interpret and are not amenable to statistical analysis. In this paper we provide a refined geometric analysis and a new statistical analysis that show that DPCP can tolerate as many outliers as the square of the number of inliers, thus improving upon other provably correct robust PCA methods. We also propose a scalable Projected Sub-Gradient Descent method (DPCP-PSGD) for solving the DPCP problem and show it admits linear convergence even though the underlying optimization problem is non-convex and non-smooth. Experiments on road plane detection from 3D point cloud data demonstrate that DPCP-PSGD can be more efficient than the traditional RANSAC algorithm, which is one of the most popular methods for such computer vision applications.

Can we utilize reinforcement learning to automatically form the abstractions needed for planning, thus obtaining the best of both approaches?

A specialized glass layer could make tomorrow's computers faster and more energy efficient. An early version of the glass substrate developed by Absolics. Human-made glass is thousands of years old. But it's now poised to find its way into the AI chips used in the world's newest and largest data centers. This year, a South Korean company called Absolics is planning to start commercial production of special glass panels designed to make next-generation computing hardware more powerful and energy efficient. Other companies, including Intel, are also pushing forward in this area.

In this paper we consider the case of generativemodels. In particular,we investigate generativeadversarialnetworks (GANs) in the task of learning new categories in a sequential fashion.

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When you purchase through links in our articles, we may earn a small commission. ChatPlayground AI lets you run a single prompt across multiple top AI models and compare the results instantly--now just $79 for lifetime access. Using AI tools can feel a bit like a juggling act. One model might be great for brainstorming, another for writing code, and another for summarizing documents. Before long, you're bouncing between platforms just to compare results.

The errors that quantum computers make are holding the technology back. Quantum computers won't be truly useful until they can correct their mistakes Quantum computers are already here, but they make far too many errors. This is arguably the biggest obstacle to the technology really becoming useful, but recent breakthroughs suggest a solution may be on the horizon. Errors creep into traditional computers too, but there are well-established techniques for correcting them. They rely on redundancy, where extra bits are used to detect when 0s incorrectly swap to 1s or vice versa.

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