In Figure 2, we examine the probability of acquiring a '7' as a function of the number of acquired We see that XWED initially focuses on 7s but then diversifies. The XWED behavior is preferable: we are initially unsure about the loss of these points, but once the loss is well characterized for the 7s we should explore other areas as well. B.2 Constant π Fails for Distribution Shift. Figure B.1 (a) shows that, for LURE suffered high variance in Figure 3. In Figure B.1 (b), we observe that ASE continues to Figure B.2 demonstrates that ASEs continue to outperform all other baselines for the task of This result highlights the importance of the adaptive nature of both ASE-and LUREbased active testing. Figure B.2: V ariant of the experiments of 7.3 where we estimate the accuracy of the main model. We here investigate a variation of the experiments in 7.3: reducing the size of the training set to Despite this, Figure B.3 demonstrates that ASEs continue to outperform all baselines.

Writing about smart home technology, smart devices, and voice assistants is my job. Yet, I don't remember the last time I actually spoke with Alexa. Just to be clear, I don't mean to pick on Alexa per se. I rarely speak to Google Assistant or Apple's Siri, either. It's way easier to haul out my phone and use an app than it is to get a supposedly "smart" voice assistant to do what I want.

A cyber kidnapping scam startled one mom into believing her daughter was making a pleading call to her after being kidnapped, but it was all an illusion to not only con the family out of money, but potentially abduct her [the mother] as well. "It was a back-and-forth," Jennifer DeStefano, a mom from Arizona, said Sunday on "Fox & Friends Weekend." "She called me crying and sobbing. I asked her what happened. She said, 'Mom, I messed up.' I said, 'Okay, what did you do? And then this man told her to put her head back, and then I got concerned. She said, 'Mom, these bad men have me, help me, help me, help me.' Then the phone fades off as this man gets on the phone and tells me, 'We have her.' It sounded as if the phone was being ripped out of her hand in that process of the conversation."

Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages. However, LVLMs still suffer from object hallucination, which is the problem of generating descriptions that include objects that do not actually exist in the images. This can negatively impact many vision-language tasks, such as visual summarization and reasoning. To address this issue, we propose a simple yet powerful algorithm, LVLM Hallucination Revisor (LURE), to post-hoc rectify object hallucination in LVLMs by reconstructing less hallucinatory descriptions. LURE is grounded in a rigorous statistical analysis of the key factors underlying object hallucination, including co-occurrence (the frequent appearance of certain objects alongside others in images), uncertainty (objects with higher uncertainty during LVLM decoding), and object position (hallucination often appears in the later part of the generated text). LURE can also be seamlessly integrated with any LVLMs. We evaluate LURE on six open-source LVLMs, achieving a 23% improvement in general object hallucination evaluation metrics over the previous best approach. In both GPT and human evaluations, LURE consistently ranks at the top. Our data and code are available at https://github.com/YiyangZhou/LURE.

Deep neural networks (DNNs) are often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data often arrive in chunks over time. This leads to important considerations about the optimal strategy for training DNNs, such as whether to fine-tune them with each chunk of incoming data (warm-start) or to retrain them from scratch with the entire corpus of data whenever a new chunk is available. While employing the latter for training can be resource-intensive, recent work has pointed out the lack of generalization in warm-start models. Therefore, to strike a balance between efficiency and generalization, we introduce Learn, Unlearn, and Relearn (LURE) an online learning paradigm for DNNs. LURE interchanges between the unlearning phase, which selectively forgets the undesirable information in the model through weight reinitialization in a data-dependent manner, and the relearning phase, which emphasizes learning on generalizable features. We show that our training paradigm provides consistent performance gains across datasets in both classification and few-shot settings. We further show that it leads to more robust and well-calibrated models.

Take a walk in French Guiana's tropical rainforests, and you'll encounter giant spiderwebs longer than a school bus. Inside, thousands of tiny, quarter-inch-long spiders wait for their prey to be trapped, allowing the predators to rush to overwhelm their victims. "In groups, they can capture prey up to 700 times [heavier] than each individual spider," such as moths and grasshoppers, says Raphaël Jeanson, an ethologist who studies the behavior of animals in their natural environment at the Center for Integrative Biology in Toulouse, France. Anelosimus eximius is a so-called "social" spider that lives in large, cooperative colonies--an extremely rare lifestyle for spiders. Each amber-colored South American spider is smaller than a ladybug, and even when they're hunting together, they pose no threat to people.

Farquhar et al. [2021] show that correcting for active learning bias with underparameterised models leads to improved downstream performance. For overparameterised models such as NNs, however, correction leads either to decreased or unchanged performance. They suggest that this is due to an "overfitting bias" which offsets the active learning bias. We show that depth uncertainty networks operate in a low overfitting regime, much like underparameterised models. They should therefore see an increase in performance with bias correction. Surprisingly, they do not. We propose that this negative result, as well as the results Farquhar et al. [2021], can be explained via the lens of the bias-variance decomposition of generalisation error.

Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution. We formalize this bias and investigate the situations in which it can be harmful and sometimes even helpful. We further introduce novel corrective weights to remove bias when doing so is beneficial. Through this, our work not only provides a useful mechanism that can improve the active learning approach, but also an explanation of the empirical successes of various existing approaches which ignore this bias. In particular, we show that this bias can be actively helpful when training overparameterized models -- like neural networks -- with relatively little data.

Meet AguaDrone – the world's only waterproof drone platform built for marine research, aquaculture, sport, and commercial fishing. Throughout history, humans have been using various fishing methods. Over the years, the tools that we use became pretty sophisticated. It was only a matter of time before someone would invent a quadcopter drone designed specifically for fishermen. It's created by the company AguaDrone and it is the first drone that can autonomously fish in deep waters.