Nvidia's CEO Jensen Huang says of the idea there is an artificial intelligence bubble, From our vantage point, we see something very different." Nvidia delivered a surprisingly strong revenue forecast and pushed back on the idea that the artificial intelligence industry is in a bubble, easing concerns that had spread across the tech sector. Sales will be about $65 billion in the January quarter, the chipmaker said in a statement on Wednesday. Analysts had estimated $62 billion on average, according to data compiled by Bloomberg. The outlook signals that demand remains robust for Nvidia's artificial intelligence accelerators, the pricey and powerful chips used to develop AI models. Nvidia has faced growing fears that the runaway spending on such equipment isn't sustainable.

Record sales, a strong financial forecast, and CEO Jensen Huang's impassioned arguments on his company's earnings call weren't enough to push Nvidia shares back to their October high. Nvidia CEO Jensen Huang speaks to the media in Tainan, Taiwan on November 7, 2025. Nvidia CEO Jensen Huang didn't need any prompting on Wednesday to address the elephant in the room . "There's been a lot of talk about an AI bubble," he said on an earnings call before quickly getting to his main point: "From our vantage point, we see something very different." Huang went on to spend about five minutes trying to explain how the chipmaker, which has soared to become the world's most valuable publicly traded company over the past three years, would be able to sustain unprecedented customer demand.

Preparing training data for deep vision models is a labor-intensive task.

Then, we propose FreTS, a simple yet effective architecture built upon Fre quency-domain MLPs for T ime S eries forecasting.

Since diffusion models require the iterative sampling on high dimensional space, they are computationally expansive and time consuming.

Privacy has nowadays become a major concern in large-scale data processing. Releasing seemingly harmless statistics of a dataset could unexpectedly leak sensitive information of individuals (see e.g.

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The central task of interpretability research is to explain the functions that AI systems learn from data. Investigating these functions requires experimentation with trained models, using tools that incorporate varying degrees of human input. Hand-tooled approaches that rely on close manual inspection [Zeiler and Fergus, 2014, Zhou et al., 2014, Mahendran and V edaldi, 2015, Olah et al., 2017, 2020, Elhage et al., 2021] or search for predefined phenomena [Wang et al., 2022, Nanda

In high-dimensional statistics, the Lasso is a cornerstone method for simultaneous variable selection and parameter estimation. However, its reliance on the squared loss function renders it highly sensitive to outliers and heavy-tailed noise, potentially leading to unreliable model selection and biased estimates. To address this limitation, we introduce the Exponential Lasso, a novel robust method that integrates an exponential-type loss function within the Lasso framework. This loss function is designed to achieve a smooth trade-off between statistical efficiency under Gaussian noise and robustness against data contamination. Unlike other methods that cap the influence of large residuals, the exponential loss smoothly redescends, effectively downweighting the impact of extreme outliers while preserving near-quadratic behavior for small errors. We establish theoretical guarantees showing that the Exponential Lasso achieves strong statistical convergence rates, matching the classical Lasso under ideal conditions while maintaining its robustness in the presence of heavy-tailed contamination. Computationally, the estimator is optimized efficiently via a Majorization-Minimization (MM) algorithm that iteratively solves a series of weighted Lasso subproblems. Numerical experiments demonstrate that the proposed method is highly competitive, outperforming the classical Lasso in contaminated settings and maintaining strong performance even under Gaussian noise. Our method is implemented in the \texttt{R} package \texttt{heavylasso} available on Github: https://github.com/tienmt/heavylasso

Hu et al. [1] suggested using uncrewed vehicles in civil infrastructure asset management. Similarly, Bechtsis et al. [2] propose using uncrewed ground vehicles (UGVs) in precision farming. One of the emerging areas where such vehicles can prove helpful is assisting in postdisaster evacuation. Natural disasters, including earthquakes, tsunamis, hurricanes, and volcanic eruptions, can severely damage the urban infrastructure, leading to considerable losses. Following such events, providing timely relief and disseminating crucial information, such as safe evacuation routes, becomes essential for affected individuals' safe and organized movement. Recently, among the advanced technologies integrated into disaster response missions include uncrewed aerial vehicles (UAVs) that have been crucial in assessing the state of critical infrastructure essential services, including telecommunications, transportation, and buildings, to facilitate efficient disaster response and evacuation [3]. UAV systems have proven to be increasingly valuable in disaster relief and emergency response (DRER) efforts by enhancing the capabilities of the first responders, offering advanced predictive insights, and enabling early warning systems [4]. UAVs have assisted in diverse tasks, including remote sensing, search and rescue, forest fire detection, survey and surveillance [5].