To extend this and adapt other results to our setting, we could now apply the Simulation Lemma [1]to bound the value difference given the model error,or alternatively, develop the theory in the direction of[55]andrelated work. Code is available at https://github.com/spitis/mocoda Forexample, in2d Navigation,themaskfunction was implementedasfollows: def Mask2dNavigation(input_tensor): """ accepts B x num_sa_features, and returns B x num_parents x num_children """ # base local mask mask = torch.tensor( Theadvantageofthisapproach isthat we can easily do conditional sampling incase of overlapping parent sets. The CQL implementation uses SAC [17].

We propose a machine-learning-based methodology for in-situ weather forecast postprocessing that is both spatially coherent and multivariate. Compared to previous work, our Flow MAtching Postprocessing (FMAP) better represents the correlation structures of the observations distribution, while also improving marginal performance at the stations. FMAP generates forecasts that are not bound to what is already modeled by the underlying gridded prediction and can infer new correlation structures from data. The resulting model can generate an arbitrary number of forecasts from a limited number of numerical simulations, allowing for low-cost forecasting systems. A single training is sufficient to perform postprocessing at multiple lead times, in contrast with other methods which use multiple trained networks at generation time. This work details our methodology, including a spatial attention transformer backbone trained within a flow matching generative modeling framework. FMAP shows promising performance in experiments on the EUPPBench dataset, forecasting surface temperature and wind gust values at station locations in western Europe up to five-day lead times.

Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of out-of-distribution (OoD) data. Specifically, we propose a novel detection algorithm for detecting unknown objects in image data, which leverages the features extracted by the model from each sample. Differently from other recent approaches in the literature, our proposal does not require retraining the object detector, thereby allowing for the use of pretrained models. Our proposed OoD detector exploits the application of supervised dimensionality reduction techniques to mitigate the effects of the curse of dimensionality on the features extracted by the model. Furthermore, it utilizes high-resolution feature maps to identify potential unknown objects in an unsupervised fashion. Our experiments analyze the Pareto trade-off between the performance detecting known and unknown objects resulting from different algorithmic configurations and inference confidence thresholds. We also compare the performance of our proposed algorithm to that of logits-based post-hoc OoD methods, as well as possible fusion strategies. Finally, we discuss on the competitiveness of all tested methods against state-of-the-art OoD approaches for object detection models over the recently published Unknown Object Detection benchmark. The obtained results verify that the performance of avant-garde post-hoc OoD detectors can be further improved when combined with our proposed algorithm.

For We introduce Mirage, the first multi-level superoptimizer for a given algorithm, these optimizers automatically generate tensor programs. A key idea in Mirage is Graphs, a uniform performant kernels by searching over possible strategies for representation of tensor programs at the kernel, thread block, executing the kernel on the target hardware. However, due and thread levels of the GPU compute hierarchy. Graphs to the linear algebra nature of DNNs, a tensor program can enable Mirage to discover novel optimizations that combine be represented by a wide spectrum of mathematically equivalent algebraic transformations, schedule transformations, and algorithms, and existing schedule-based optimizers only generation of new custom kernels. To navigate the large consider kernels whose algorithms are manually specified search space, Mirage introduces a pruning technique based by users, resulting in missed optimization opportunities.

As Convolutional Neural Networks (CNNs) are increasingly being employed in safety-critical applications, it is important that they behave reliably in the face of hardware errors. Transient hardware errors may percolate undesirable state during execution, resulting in software-manifested errors which can adversely affect high-level decision making. This paper presents HarDNN, a software-directed approach to identify vulnerable computations during a CNN inference and selectively protect them based on their propensity towards corrupting the inference output in the presence of a hardware error. We show that HarDNN can accurately estimate relative vulnerability of a feature map (fmap) in CNNs using a statistical error injection campaign, and explore heuristics for fast vulnerability assessment. Based on these results, we analyze the tradeoff between error coverage and computational overhead that the system designers can use to employ selective protection. Results show that the improvement in resilience for the added computation is superlinear with HarDNN. For example, HarDNN improves SqueezeNet's resilience by 10x with just 30% additional computations.