When machine learning systems face dataset shift, model calibration plays a pivotal role in ensuring their reliability.Calibration error (CE) provides insights into the alignment between the predicted confidence scores and the classifier accuracy.While prior works have delved into the implications of dataset shift on calibration, existing CE estimators either (i) assume access to labeled data from the target domain, often unavailable in practice, or (ii) are derived under a covariate shift assumption.In this work we propose a novel, label-free, consistent CE estimator under label shift. Label shift is characterized by changes in the marginal label distribution p(Y), with a constant conditional p(X Y) distribution between the source and target. We introduce a novel calibration method, called LaSCal, which uses the estimator in conjunction with a post-hoc calibration strategy, to perform unsupervised calibration on the target distribution. Our thorough empirical analysis demonstrates the effectiveness and reliability of the proposed approach across different modalities, model architectures and label shift intensities.

As an OnlyFans creator, she's learned to live with the exhausting, infuriating cycle of impersonation that comes with the territory. Five years in, she knows the drill. But this time felt different. The account in question hit too close. Everything checks out, but that is not her face.

With his name choice and speech, Leo XIV firmly marks AI as a defining challenge facing our world today. But also embedded in the name is a potential path forward. Leo XIII, during his papacy, laid out a vision for protecting workers against tech-induced consolidation, including minimum wage laws and trade unions. His ideas soon gained influence and were implemented in government policies around the world. While it's still unclear what specific guidance Leo XIV may issue on artificial intelligence, history suggests the implications of his crusade could be profound.

'The Daily Show' mocks RFK Jr. for swimming in a sewage-infested creek Mashable Tech Science Life Social Good Entertainment Deals Shopping Games Search Cancel * * Search Result Tech Apps & Software Artificial Intelligence Cybersecurity Cryptocurrency Mobile Smart Home Social Media Tech Industry Transportation All Tech Science Space Climate Change Environment All Science Life Digital Culture Family & Parenting Health & Wellness Sex, Dating & Relationships Sleep Careers Mental Health All Life Social Good Activism Gender LGBTQ Racial Justice Sustainability Politics All Social Good Entertainment Games Movies Podcasts TV Shows Watch Guides All Entertainment SHOP THE BEST Laptops Budget Laptops Dating Apps Sexting Apps Hookup Apps VPNs Robot Vaccuums Robot Vaccum & Mop Headphones Speakers Kindles Gift Guides Mashable Choice Mashable Selects All Sex, Dating & Relationships All Laptops All Headphones All Robot Vacuums All VPN All Shopping Games Product Reviews Adult Friend Finder Bumble Premium Tinder Platinum Kindle Paperwhite PS5 vs PS5 Slim All Reviews All Shopping Deals Newsletters VIDEOS Mashable Shows All Videos Home Entertainment TV Shows'The Daily Show' mocks RFK Jr. for swimming in a sewage-infested creek "These pictures are so wild, the fact that he went swimming in jeans is the most normal part of this story." By Sam Haysom Sam Haysom Sam Haysom is the Deputy UK Editor for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Read Full Bio on May 15, 2025 Share on Facebook Share on Twitter Share on Flipboard Watch Next John Oliver slams RFK Jr. in powerful public health deep dive'Enola Gay No Homo': 'The Daily Show' mocks Trump administration's'sloppy' anti-DEI measures 9:36 'The Daily Show' gleefully mocks Trump officials' scrambling Signal chat excuses'The Daily Show' mocks Trump over tariff pause RJK Jr. has made headlines for many gross and disturbing things, so the U.S. health secretary's recent decision totake his grandkids swimming in a sewage-infested creek didn't come as a major shock to The Daily Show. "At this point it's like RFK Jr. is going out of his way to be gross," says host Jordan Klepper in the clip above.

There is a little shuttle bus in the Inland Empire that's fueled with big aspirations. It's electric, tops out at 25 mph, and can only go on a pre-designated route set up by the Riverside Transit Agency. But here's a catch -- it also drives itself. As of Monday, commuters in Riverside are the first in the country to ride a fully self-driving, publicly accessible bus that is deployed by a city transit agency. "I like to say I have no lesser ambition than to be the new Detroit for vehicle manufacturing," Riverside Mayor Lock Dawson said.

Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically. An algorithm called feedback alignment achieves deep learning without weight transport by using random feedback weights, but it performs poorly on hard visual-recognition tasks. Here we describe two mechanisms -- a neural circuit called a weight mirror and a modification of an algorithm proposed by Kolen and Pollack in 1994 -- both of which let the feedback path learn appropriate synaptic weights quickly and accurately even in large networks, without weight transport or complex wiring. Tested on the ImageNet visual-recognition task, these mechanisms outperform both feedback alignment and the newer sign-symmetry method, and nearly match backprop, the standard algorithm of deep learning, which uses weight transport.

As reinforcement learning agents become increasingly deployed in real-world scenarios, predicting future agent actions and events during deployment is important for facilitating better human-agent interaction and preventing catastrophic outcomes. This paper experimentally evaluates and compares the effectiveness of future action and event prediction for three types of RL agents: explicitly planning, implicitly planning, and non-planning. We employ two approaches: the inner state approach, which involves predicting based on the inner computations of the agents (e.g., plans or neuron activations), and a simulation-based approach, which involves unrolling the agent in a learned world model. Our results show that the plans of explicitly planning agents are significantly more informative for prediction than the neuron activations of the other types. Furthermore, using internal plans proves more robust to model quality compared to simulation-based approaches when predicting actions, while the results for event prediction are more mixed.

TL;DR: Put all your AI tools like ChatGPT, Gemini Pro, and Leonardo.AI in one place with a lifetime subscription to 1minAI, an all-in-one AI app, on sale for just 39.99 (reg. The free version of some AI models like ChatGPT can get the job done, but if you want the good stuff, you should consider opting for a paid subscription. A lifetime subscription to 1minAI usually costs 234, but you can get one on sale now for 39.99. You don't just get the baseline version, either -- 1minAI users can chat with GPT-4, GPT-4 Turbo, Gemini Pro 1.5, and Llama 2 or Llama 3. Like a ton of other AI platforms, 1minAI has a limit to how much you can generate every month. Unlike other platforms, the limit is incredibly high.

There's always a catch: These free courses do not come with a shareable certificate of completion or graded assignments/exams. But you can start learning at a pace that suits you, so there's really nothing stopping you from enrolling. Find the best free online courses from MIT on edX.

Pipeline parallelism has been widely explored, but most existing schedules lack a systematic methodology. In this paper, we propose a framework to decompose pipeline schedules as repeating a building block, and show that the lifespan of the building block decides the peak activation memory of the pipeline schedule. Guided by the observations, we find that almost all existing pipeline schedules, to the best of our knowledge, are memory inefficient. To address this, we introduce a family of memory efficient building blocks with controllable activation memory, which can reduce the peak activation memory to 1/2 of 1F1B without sacrificing efficiency, and even to 1/3 with comparable throughput. We can also achieve almost zero pipeline bubbles while maintaining the same activation memory as 1F1B.