Recent years have witnessed a surge of approaches to use neural networks to help tackle combinatorial optimization problems, including graph optimization problems. However, theoretical understanding of such approaches remains limited. In this paper, we consider the geometric setting, where graphs are induced by points in a fixed dimensional Euclidean space. It turns out that several graph optimization problems can be approximated (in a bicriteria manner) by an algorithm that runs in time linear in graph size n via a framework that we call the Baker-paradigm. A key advantage of the Baker-paradigm is that it decomposes the input problem into (at most linear number of) small sub-problems of bounded sizes (independent of the size of the input). For the family of such bounded-size sub-problems, we can now design neural networks with universal approximation guarantees to solve them. This leads to a mixed algorithmic-ML framework, which we call NN-Baker that has the capacity to approximately solve a family of graph optimization problems (e.g, maximum independent set and minimum vertex cover) in time linear in the input graph size. We instantiate our NN-Baker by a CNN version and GNN version, and demonstrate the effectiveness and efficiency of our approach via a range of experiments.

We present a consistent algorithm for constrained classification problems where the objective (e.g.

Rebellion Studios' Atomfall is a step in the right direction as we (hopefully) near the end of an era where every big-budget RPG feels like a 60-to-80-hour commitment. After Assassin's Creed Valhalla pushed the boundaries of just how much game a game could have -- and not necessarily for the better -- it's refreshing to see an action RPG that actually lets you slow down, take a breath, and just exist in its world for a minute. Rather than drowning you in an endless sea of map markers, side quests, and fetch missions that feel more like a to-do list than an adventure, Atomfall offers something different. It's a game that trusts you to explore at your own pace rather than constantly screaming at you to engage with yet another system or mechanic. Yet while Atomfall never overstays its welcome, there's still a part of me caught between seeing its simplicity as an element that allows it to shine, or as a weakness. Warning: There are minor story spoilers ahead.

Novelty detection is a fundamental task of machine learning which aims to detect abnormal (i.e.

The curse of dimensionality is a widely known issue in reinforcement learning (RL). In the tabular setting where the state space S and the action space A are both finite, to obtain a nearly optimal policy with sampling access to a generative model, the minimax-optimal sample complexity scales linearly with |S| |A|, which can be prohibitively large when S or A is large. This paper considers a Markov decision process (MDP) that admits a set of state-action features, which can linearly express (or approximate) its probability transition kernel. We show that a model-based approach (resp. Q-learning) provably learns an ε-optimal policy (resp.

We printed a checkerboard with a 9x10 grid of blocks, each measuring 87 mm x 87 mm. Parameter Value Model Architecture Panoptic-PolarNet Test Batch Size 2 Val Batch Size 2 Test Batch size 1 post proc threshold 0.1 post proc nms kernel 5 post proc top k 100 center loss MSE offset loss L1 center loss weight 100 offset loss weight 10 enable SAP True SAP start epoch 30 SAP rate 0.01 Table 3: Parameters for Panoptic Segmentation model Model mIoU (%) Semantic Segmentation Cylinder3D 67.8 Panoptic Segmentation Panoptic-PolarNet 59.5 4D Panoptic Segmentation 4D-StOP 58.8 Table 6: Models of various tasks used in our experiments and their performances on SemanticKITTI The results reveal a significant variance in performance across different categories. The dataset is divided into 17 and 6 categories, respectively. Ground' and'Roads', as opposed to grouping anything related to ground as a single category. Overall, the performance across these tasks underscores the challenges posed by our dataset's With our dataset, future work can focus on improving the model's capacity to handle such diverse The raw data, processed data, and framework code can be found on our website.

In this paper, we introduce the USCILab3D dataset, a large-scale, annotated outdoor dataset designed for versatile applications across multiple domains, including computer vision, robotics, and machine learning.