Below, we present the pseudo-code for DQN Pro. Pro is minimal (highlighted in gray). Sticky actions True Optimizer Adam Kingma & Ba (2015) Network architecture Nature DQN network Mnih et al. (2015) Random seeds { 0, 1, 2, 3, 4 } Rainbow hyper-parameters (shared) Batch size 64 Other Config file rainbow_aaai.gin Theorem 2. Consider the PMPI algorithm specified by: We make two assumptions: 1. we assume error in policy evaluation step, as already stated in equation (4). All results are averaged over 5 independent seeds.

We present the first algorithm for testing equivalence between two continuous distributions using differential privacy (DP).

LiDAR point clouds, representing detailed three-dimensional descriptions of natural and built environments, are widely used in applications such as updating digital elevation models, monitoring glaciers and landslides, shoreline analysis, and urban development. A crucial step in these applications is the classification of 3D LiDAR points into semantic categories such as vegetation, man-made structures, and water. In our previous work [5], we introduced product coefficients as measure-theoretic descriptors that enrich LiDAR data with local structural information. Computed on dyadic neighborhoods around each point, these coefficients capture geometric variability beyond raw spatial coordinates.

We present the first algorithm for testing equivalence between two continuous distributions using differential privacy (DP).

Sony and developer Naughty Dog got an earful back in 2022 when they announced The Last of Us Part I, a ground-up PS5 remake of the 2013 title that was originally released on the PS3 (and then remastered a year later for the just-launched PS4). Most of that came down to the 70 price tag. Yes, the game looked incredible, there were some new modes added for super-fans and enemy AI had been upgraded, but the level design and gameplay were identical to the original. Plenty of people fairly called it a money grab. The good news that The Last of Us Part II Remastered, announced back in November, escapes that tag for two important reasons.

Sequential Monte Carlo methods can characterize arbitrary distributions using sequential importance sampling and resampling, but typically require very large sample sizes to mitigate weight collapse [Snyder et al., 2008, 2015]. By contrast, ensemble Kalman-type methods avoid the use of weights, but are based on affine prior-to-posterior updates that are consistent only if all distributions involved are Gaussian. In the context of smoothing, such methods include the ensemble Kalman smoother (EnKS) [Evensen and Van Leeuwen, 2000], which has inspired numerous algorithmic variations such as the ensemble smoother with multiple data assimilation [Emerick and Reynolds, 2013] and the iterative ensemble Kalman smoother (iEnKS) [Bocquet and Sakov, 2014, Evensen et al., 2019], as well as backwards smoothers such as the ensemble Rauch-Tung-Striebel smoother (EnRTSS) [Raanes, 2016]. These two classes of methods occupy opposite ends of a spectrum that ranges from an emphasis on statistical generality at one end to an emphasis on computational efficiency at the other. This trade-off complicates design decisions for smoothing problems that are at once non-Gaussian and computationally expensive.

DALL-E, Midjourney, and Stable Diffusion are among the generative AI technologies widely used to produce images in response to user prompts. The output images are, for the most part, indistinguishable from images humans might have created. Generative AI systems are capable of producing human-creator-like images because of the extremely large quantities of images, paired with textual descriptions of the images' contents, on which the systems' image models were trained. A text prompt to compose a picture of a dog playing with a ball on a beach at sunset will generate a responsive image drawing upon embedded representations of how dogs, balls, beaches, and sunsets are typically depicted and arranged in images of this sort.