Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured data. However, for node classification tasks, often, only marginal improvement of GNNs has been observed in practice over their linear counterparts. Previous works provide very few understandings of this phenomenon. In this work, we resort to Bayesian learning to give an in-depth investigation of the functions of non-linearity in GNNs for node classification tasks. Given a graph generated from the statistical model CSBM, we observe that the max-a-posterior estimation of a node label given its own and neighbors' attributes consists of two types of non-linearity, the transformation of node attributes and a ReLU-activated feature aggregation from neighbors.

SAVE 18%: As of May 14, you can get the Narwal Freo Z10 Robot Vacuum and Mop for 899.99, down from 1,099.99, at Amazon. It's also the lowest price we've seen for this model yet. Paying over a grand for a robot vacuum is a little ridiculous, if you can get one with all the bells and whistles for less. The Narwal Freo Z10 Robot Vacuum and Mop (one of Narwal's newest releases) is currently on sale for 899.99 (with an on-screen coupon), down from 1,099.99. It's also the lowest price we've ever seen for this model. If you haven't heard of it, Narwal is known for its AI-powered cleaning robots.

The incorporation of cutting planes within the branch-and-bound algorithm, known as branch-and-cut, forms the backbone of modern integer programming solvers. These solvers are the foremost method for solving discrete optimization problems and thus have a vast array of applications in machine learning, operations research, and many other fields. Choosing cutting planes effectively is a major research topic in the theory and practice of integer programming. We conduct a novel structural analysis of branch-and-cut that pins down how every step of the algorithm is affected by changes in the parameters defining the cutting planes added to the input integer program. Our main application of this analysis is to derive sample complexity guarantees for using machine learning to determine which cutting planes to apply during branch-and-cut.

Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labeling and many more. In the correlation clustering problem one receives as input a set of nodes and for each node a list of co-clustering preferences, and the goal is to output a clustering that minimizes the disagreement with the specified nodes' preferences. In this paper, we introduce a simple and computationally efficient algorithm for the correlation clustering problem with provable privacy guarantees. Our additive error is stronger than those obtained in prior work and is optimal up to polylogarithmic factors for fixed privacy parameters.

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design.

Ensembling neural networks is an effective way to increase accuracy, and can often match the performance of individual larger models. This observation poses a natural question: given the choice between a deep ensemble and a single neural network with similar accuracy, is one preferable over the other? Recent work suggests that deep ensembles may offer distinct benefits beyond predictive power: namely, uncertainty quantification and robustness to dataset shift. In this work, we demonstrate limitations to these purported benefits, and show that a single (but larger) neural network can replicate these qualities. First, we show that ensemble diversity, by any metric, does not meaningfully contribute to an ensemble's ability to detect out-of-distribution (OOD) data, but is instead highly correlated with the relative improvement of a single larger model.

In addition to gobbling up most of the internet, ChatGPT now wants access to your OneDrive and SharePoint files, too. One of the earliest uses of AI was to summarize documents and folders of documents, and there's only so many times you can ask it whether Spider-Man would beat Wonder Woman in a fair fight. It would be more productive for AI to collate and make sense of your own personal information, assuming you want to grant access to it. According to OpenAI, ChatGPT can now connect to your OneDrive or SharePoint document libraries, assuming you're a paid ChatGPT Plus, Pro, or Team user who lives outside the EEA, Switzerland, and the UK (via Windows Central). You'll obviously have to connect ChatGPT and give it permission to start poring over your cloud documents.

It might be time to say your goodbyes to the iconic "I'm Feeling Lucky" button below the Google Search bar. In its place will be AI Mode, a feature that's been quietly rolling out to users this week, according to The Verge. It's part of Google's ongoing push to merge its core search engine with Gemini, its flagship AI model. First announced in March, AI Mode started as an experimental opt-in via Google Labs. Earlier this May, it became available to all Labs users.

That photo you'd like to share on TikTok seems a bit blah. If only there were some way you could make it more exciting, dynamic, and visual. You can, thanks to a new AI-powered image-to-video feature known as AI Alive. Unveiled on Tuesday, AI Alive creates a brief video clip out of any still photo. Available within TikTok's Story Camera, the AI tool taps into AI to automatically add the right prompt and transform your photo.

Breakthroughs, discoveries, and DIY tips sent every weekday. Archaeopteryx represents a pivotal point in the grand evolutionary journey linking dinosaurs to their avian descendants. But paleontologists still have questions about the Jurassic era animal's anatomy and behavior roughly 165 years after its discovery. One of the most pressing lingering mysteries is how Archaeopteryx managed to fly above its fellow feathered dinosaur relatives. After more than two decades spent in a private collection, one of the most detailed and complete fossil sets arrived at the Chicago's Field Museum in 2022.