Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including the key tasks of removing biases (RB), resolving confusion (RC) (caused by mislabelled data in trained models), as well as allowing users to exercise their'right to be forgotten' to protect User Privacy (UP). This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for'forgetting' and associated metrics for forget quality. For UP, we propose a novel adaptation of a strong Membership Inference Attack for unlearning. We also propose SCRUB, a novel unlearning algorithm, which is the only method that is consistently a top performer for forget quality across the different application-dependent metrics for RB, RC, and UP. At the same time, SCRUB is also consistently a top performer on metrics that measure model utility (i.e.

Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set.

Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including the key tasks of removing biases (RB), resolving confusion (RC) (caused by mislabelled data in trained models), as well as allowing users to exercise their'right to be forgotten' to protect User Privacy (UP). This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for'forgetting' and associated metrics for forget quality. For UP, we propose a novel adaptation of a strong Membership Inference Attack for unlearning. We also propose SCRUB, a novel unlearning algorithm, which is the only method that is consistently a top performer for forget quality across the different application-dependent metrics for RB, RC, and UP. At the same time, SCRUB is also consistently a top performer on metrics that measure model utility (i.e.

Machine learning models are integrated into many real-world applications. Since these models contain artifacts of potentially sensitive training data, this raises concerns about data confidentiality and user privacy. The ability to remove specific training data from a model has emerged as a key mechanism to enforce, for instance, the "right to be forgotten" promoted by the European GDPR law [17] or the "right to erase" in the Canadian CPPA legislation [29]. Approximate machine unlearning aims to find efficient mechanisms, avoiding the cost of learning a new model from scratch over the training dataset deprived of the sensitive data. Privacy is not the only application of machine unlearning. It has been proven useful as a defense against backdoor attacks by annihilating the influence of the poisoned training data [45, 38], or to improve fairness by removing data that induce biases in the training set. Another scenario is the derivation of a restricted public model from a powerful private model learned on some sensitive data [12]. The accuracy of the public model should be on par with or slightly degraded compared to the private model.

Beginning today, Microsoft is debuting Copilot Mode for Edge, an experimental version of Edge that places Copilot front and center on new tabs and allows it to see the content on all of your open tabs. Tip-toeing into this space, Microsoft is describing Copilot Mode for Edge as "experimental" and "free for a limited time," suggesting that it will eventually become part of a paid subscription. Usage limits will apply, though Microsoft isn't saying what they are yet. Microsoft is also saying that Copilot Mode is optional, opt-in, and can be flipped on or off via the Settings panel at any time. Turn it on, however, and the "new tab" page -- which typically mimics the Windows widgets panel with news and other content supplied by MSN and other publications -- will be replaced with a simple Copilot box like the one above, similar to the box on the Copilot app itself.

Before we know it summer will be here, but don't wait for the hotter days to arrive to realize that your pool is in a state of disarray. The Aiper Scuba X1, priced at 1,399, is an ideal solution to help get your pool ready for action. The Scuba X1 is essentially the full package when it comes to cleaning your pool, taking care of everything for you. Aiper's robotic pool cleaner can provide everyday maintenance, saving you countless hours and money spent on other solutions. Each morning or evening, you can automate the Aiper Scuba X1 to scrub the pool bottom and walls free of algae and dirt, and scrub the waterline for any hair, leaves, sticks, and any other grime that blew into or otherwise ended up in the water.

Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including the key tasks of removing biases (RB), resolving confusion (RC) (caused by mislabelled data in trained models), as well as allowing users to exercise their'right to be forgotten' to protect User Privacy (UP). This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for'forgetting' and associated metrics for forget quality. For UP, we propose a novel adaptation of a strong Membership Inference Attack for unlearning. We also propose SCRUB, a novel unlearning algorithm, which is the only method that is consistently a top performer for forget quality across the different application-dependent metrics for RB, RC, and UP.

It's spring cleaning season, but that doesn't mean you need to get on the floor and scrub. Robot vacuums are a great way to keep your home clean while doing little to nothing, and a few robovacs from Shark are currently on sale. One of the best deals comes courtesy of a 44 percent discount on Shark's AI Robot Vacuum and Mop. The device is down to 270 from 480 -- only 20 more than its all-time low price. Shark's AI Robot Vacuum and Mop is a great option for anyone looking to try a robot vacuum or upgrade their entry-level model. It's nearly identical to Shark's much pricier Ultra 2-in-1 Robot Vacuum and Mop, which appears on our list of the best robot vacuums for 2024 -- it just doesn't have a self-emptying base.

Deep machine unlearning is the problem of `removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including the key tasks of removing biases (RB), resolving confusion (RC) (caused by mislabelled data in trained models), as well as allowing users to exercise their `right to be forgotten' to protect User Privacy (UP). This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for `forgetting' and associated metrics for forget quality. For UP, we propose a novel adaptation of a strong Membership Inference Attack for unlearning. We also propose SCRUB, a novel unlearning algorithm, which is the only method that is consistently a top performer for forget quality across the different application-dependent metrics for RB, RC, and UP. At the same time, SCRUB is also consistently a top performer on metrics that measure model utility (i.e. accuracy on retained data and generalization), and is more efficient than previous work. The above are substantiated through a comprehensive empirical evaluation against previous state-of-the-art.

3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations. However, typical 3DSGs contain hundreds of objects and symbols even for small environments; rendering task planning on the full graph impractical. We construct TASKOGRAPHY, the first large-scale robotic task planning benchmark over 3DSGs. While most benchmarking efforts in this area focus on vision-based planning, we systematically study symbolic planning, to decouple planning performance from visual representation learning. We observe that, among existing methods, neither classical nor learning-based planners are capable of real-time planning over full 3DSGs. Enabling real-time planning demands progress on both (a) sparsifying 3DSGs for tractable planning and (b) designing planners that better exploit 3DSG hierarchies. Towards the former goal, we propose SCRUB, a task-conditioned 3DSG sparsification method; enabling classical planners to match and in some cases surpass state-of-the-art learning-based planners. Towards the latter goal, we propose SEEK, a procedure enabling learning-based planners to exploit 3DSG structure, reducing the number of replanning queries required by current best approaches by an order of magnitude. We will open-source all code and baselines to spur further research along the intersections of robot task planning, learning and 3DSGs.