This section provides more details on the augmentation process of Figure 1. For Image Filtering (IF), s equals to 1.5, so the image is blurred by convolving with K = 1.5 G3+ Testing sets are not involved in our augmentation search process. ImageNet [2] is a challenging large scale dataset, containing about 1.28 million training The testing set is not used. Mean values and standard deviations are reported. The hyperparameters for re-training used in this paper are listed in Tab.

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph.

OpenAI has fully acquired Io, a joint venture it cocreated last year with Jony Ive, the famed British designer behind the sleek industrial aesthetic that defined the iPhone and more than two decades of Apple products. In a nearly 10-minute video posted to X on Wednesday, Ive and OpenAI CEO Sam Altman said the Apple pioneer's "creative collective" will "merge with OpenAI to work more intimately with the research, engineering, and product teams in San Francisco." OpenAI says it's paying 5 billion in equity to acquire Io. The promotional video included musings on technology from both Ive and Altman, set against the golden-hour backdrop of the streets of San Francisco, but the two never share exactly what it is they're building. "We look forward to sharing our work next year," a text statement at the end of the video reads.

Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a biasonly model to adjust the learning target. In this paper, we focus on the bias-only model in these ensemble-based methods, which plays an important role but has not gained much attention in the existing literature. Theoretically, we prove that the debiasing performance can be damaged by inaccurate uncertainty estimations of the bias-only model. Empirically, we show that existing bias-only models fall short in producing accurate uncertainty estimations. Motivated by these findings, we propose to conduct calibration on the bias-only model, thus achieving a three-stage ensemble-based debiasing framework, including bias modeling, model calibrating, and debiasing. Experimental results on NLI and fact verification tasks show that our proposed three-stage debiasing framework consistently outperforms the traditional two-stage one in out-of-distribution accuracy.

This work brings together three active research areas: variational inference, variational Kalman filtering, and gradient flows on the Wasserstein space.

A.2 Mercury Data Distribution and Customized Data Structures Except for all built-in Python data structures, Mercury imports another two structures to enhance the diversity and complexity as shown in Figure 4. Table 6: Mercury-eval encompasses 256 tasks, the difficulty of which has been balanced for model evaluation. Mercury-train Figure 4: Mercury supports two customized comprises the remaining 1,633 tasks for data structures: TreeNode and ListNode. Each executed code within the sandbox is subject to certain constraints to ensure fair utilization of resources and to prevent any single code from monopolizing the system resource. Specifically, there are two primary constraints: a time limit and a memory limit. The time limit restricts how long the code can execute before being forcibly terminated, thereby ensuring that no infinite loops or excessively long computations negatively impact the availability of the sandbox.

Michael Dell is pitching a "decentralized" future for artificial intelligence that his company's devices will make possible. "The future of AI will be decentralized, low-latency, and hyper-efficient," predicted the Dell Technologies founder, chairman, and CEO in his Dell World keynote, which you can watch on YouTube. "AI will follow the data, not the other way around," Dell said at Monday's kickoff of the company's four-day customer conference in Las Vegas. Dell is betting that the complexity of deploying generative AI on-premise is driving companies to embrace a vendor with all of the parts, plus 24-hour-a-day service and support, including monitoring. On day two of the show, Dell chief operating officer Jeffrey Clarke noted that Dell's survey of enterprise customers shows 37% want an infrastructure vendor to "build their entire AI stack for them," adding, "We think Dell is becoming an enterprise's'one-stop shop' for all AI infrastructure."

The race to deploy AI agents is heating up. At its annual I/O developer conference yesterday, Google announced that Jules, its new AI coding assistant, is now available worldwide in public beta. The launch marks the company's latest effort to corner the burgeoning market for AI agents, widely regarded across Silicon Valley as essentially a more practical and profitable form of chatbot. Virtually every other major tech giant -- including Meta, OpenAI, and Amazon, just to name a few -- has launched its own agent product in recent months. Also: I tested ChatGPT's Deep Research against Gemini, Perplexity, and Grok AI to see which is best Originally unveiled by Google Labs in December, Jules is positioned as a reliable, automated coding assistant that can manage a broad suite of time-consuming tasks on behalf of human users. The model is "asynchronous," which, in programming-speak, means it can start and work on tasks without having to wait for any single one of them to finish.