Prediction: The $700 Billion Artificial Intelligence (AI) Capex Boom Will Create the Best Buying Opportunity of 2026 for These 3 Stocks - AOL.com
Prediction: The $700 Billion Artificial Intelligence (AI) Capex Boom Will Create the Best Buying Opportunity of 2026 for These 3 Stocks AOL.com
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Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation
arXiv:2604.02779v1 Announce Type: new Abstract: -Navigation through narrow and irregular gaps is an essential skill in autonomous drones for applications such as inspection, search-and-rescue, and disaster response. However, traditional planning and control methods rely on explicit gap extraction and measurement, while recent end-to-end approaches often assume regularly shaped gaps, leading to poor generalization and limited practicality. In this work, we present a fully vision-based, end-to-end framework that maps depth images directly to control commands, enabling drones to traverse complex gaps within unseen environments. Operating in the Special Euclidean group SE(3), where position and orientation are tightly coupled, the framework leverages differentiable simulation, a Stop-Gradient
Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures
arXiv:2305.18915v1 Announce Type: cross Abstract: In this work we build upon negative results from an attempt at language modeling with predicted semantic structure, in order to establish empirical lower bounds on what could have made the attempt successful. More specifically, we design a concise binary vector representation of semantic structure at the lexical level and evaluate in-depth how good an incremental tagger needs to be in order to achieve better-than-baseline performance with an end-to-end semantic-bootstrapping language model. We envision such a system as consisting of a (pretrained) sequential-neural component and a hierarchical-symbolic component working together to generate text with low surprisal and high linguistic interpretability. We find that (a) dimensionality of the
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Learn then Decide: A Learning Approach for Designing Data Marketplaces
arXiv:2503.10773v2 Announce Type: replace Abstract: As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders' value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, ensuring truthful bidding while balancing revenue maximization with minimal price discrimination. On the theoretical side, we establish a statistical viewpoint that recasts revenue optimization as a valuation density estimation problem: we
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