One of the chronic problems of deep-learning models is shortcut learning. In a case where the majority of training data are dominated by a certain feature, neural networks prefer to learn such a feature even if the feature is not generalizable outside the training set. Based on the framework of Neural Tangent Kernel (NTK), we analyzed the case of linear neural networks to derive some important properties of shortcut learning. We defined a "feature" of a neural network as an eigenfunction of NTK. Then, we found that shortcut features correspond to features with larger eigenvalues when the shortcuts stem from the imbalanced number of samples in the clustered distribution. We also showed that the features with larger eigenvalues still have a large influence on the neural network output even after training, due to data variances in the clusters. Such a preference for certain features remains even when a margin of a neural network output is controlled, which shows that the max-margin bias is not the only major reason for shortcut learning. These properties of linear neural networks are empirically extended for more complex neural networks as a two-layer fully-connected ReLU network and a ResNet-18.

Most work on sequential learning assumes a fixed set of actions that are available all the time. However, in practice, actions can consist of picking subsets of readings from sensors that may break from time to time, road segments that can be blocked or goods that are out of stock. In this paper we study learning algorithms that are able to deal with stochastic availability of such unreliable composite actions. We propose and analyze algorithms based on the Follow-The-Perturbed-Leader prediction method for several learning settings differing in the feedback provided to the learner. Our algorithms rely on a novel loss estimation technique that we call Counting Asleep Times. We deliver regret bounds for our algorithms for the previously studied full information and (semi-)bandit settings, as well as a natural middle point between the two that we call the restricted information setting. A special consequence of our results is a significant improvement of the best known performance guarantees achieved by an efficient algorithm for the sleeping bandit problem with stochastic availability. Finally, we evaluate our algorithms empirically and show their improvement over the known approaches.

Understanding animal behaviour is central to predicting, understanding, and miti-gating impacts of natural and anthropogenic changes on animal populations andecosystems. However, the challenges of acquiring and processing long-term, eco-logically relevant data in wild settings have constrained the scope of behaviouralresearch. The increasing availability of Unmanned Aerial Vehicles (UAVs), cou-pled with advances in machine learning, has opened new opportunities for wildlifemonitoring using aerial tracking. However, the limited availability of datasets with wildanimals in natural habitats has hindered progress in automated computer visionsolutions for long-term animal tracking. Here, we introduce the first large-scaleUAV dataset designed to solve multi-object tracking (MOT) and re-identification(Re-ID) problem in wild animals, specifically the mating behaviour (or lekking) ofblackbuck antelopes. Collected in collaboration with biologists, the MOT datasetincludes over 1.2 million annotations including 680 tracks across 12 high-resolution(5.4K)

Conducting experiments and gathering data for machine learning models is a complex and expensive endeavor, particularly when confronted with limited information. Typically, extensive _experiments_ to obtain features and labels come with a significant acquisition cost, making it impractical to carry out all of them. Therefore, it becomes crucial to strategically determine what to acquire to maximize the predictive performance while minimizing costs. To perform this task, existing data acquisition methods assume the availability of an initial dataset that is both fully-observed and labeled, crucially overlooking the **partial observability** of features characteristic of many real-world scenarios. In response to this challenge, we present Partially Observable Cost-Aware Active-Learning (POCA), a new learning approach aimed at improving model generalization in data-scarce and data-costly scenarios through label and/or feature acquisition. Introducing $\mu$POCA as an instantiation, we maximise the uncertainty reduction in the predictive model when obtaining labels and features, considering associated costs.

ImageNet++, no toxic images were found, indicating that the dataset's captions are safe.

Neural Information Processing Systems http://nips.cc/

The data-driven nature of modern machine learning (ML) training routines puts pressure on data supply pipelines, which become increasingly more complex. It is common to find separate disks or whole content distribution networks dedicated to servicing massive datasets. Training is often distributed across multiple workers. This emergent complexity gives a perfect opportunity for an attackertodisrupt ML training, while remaining covert.

Volvo's Electric EX60 SUV Has a 400-Mile Range--and Rethinks the Humble Seat Belt The Swedish brand's latest computer-packed EV hopes to take on and beat the BMW iX3. Alongside the chosen few in WIRED's breakdown of the most anticipated EVs coming this year, the arrival of the Volvo EX60 has also been eagerly awaited. This is mainly because of the impressive stats surrounding the car; the headline claim is a range of more than 400 miles. Sitting between the EX40 and EX90, the new EV looks more like a sibling of the entry-level EX30, which is a good car but too fast for its own good . Plus, the reveal images here from Volvo initially seem to show that the design team has figured out a way to remove the unsightly lidar roofline bulges that in some eyes ruined the finished aesthetic of the EX90.