How can we accurately identify new memory workloads while classifying known memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various workloads is an important task to guarantee the quality of DRAM. A crucial component in the process is open-set recognition which aims to detect new workloads not seen in the training phase. Despite its importance, however, existing open-set recognition methods are unsatisfactory in terms of accuracy since they fail to exploit the characteristics of workload sequences. In this paper, we propose Acorn, an accurate open-set recognition method capturing the characteristics of workload sequences. Acorn extracts two types of feature vectors to capture sequential patterns and spatial locality patterns in memory access. Acorn then uses the feature vectors to accurately classify a subsequence into one of the known classes or identify it as the unknown class. Experiments show that Acorn achieves state-of-the-art accuracy, giving up to 37% points higher unknown class detection accuracy while achieving comparable known class classification accuracy than existing methods.

We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces. Our work is motivated by practical scenarios where the target policy needs to be deterministic due to domain requirements, such as prescription of treatment dosage and duration in medicine. Although importance sampling (IS) provides a basic principle for OPE, it is ill-posed for the deterministic target policy with continuous actions. Our main idea is to relax the target policy and pose the problem as kernel-based estimation, where we learn the kernel metric in order to minimize the overall mean squared error (MSE). We present an analytic solution for the optimal metric, based on the analysis of bias and variance. Whereas prior work has been limited to scalar action spaces or kernel bandwidth selection, our work takes a step further being capable of vector action spaces and metric optimization. We show that our estimator is consistent, and significantly reduces the MSE compared to baseline OPE methods through experiments on various domains.

几乎没有射击的对象检测（FSOD）旨在对新类别的几幅图像进行分类和检测。现有的元学习方法由于结构限制而在支持和查询图像之间的功能不足。我们提出了一个层次的注意网络，该网络具有依次大的接收场，以充分利用查询和支持图像。此外，元学习不能很好地区分类别，因为它决定了支持和查询图像是否匹配。换句话说，基于度量的分类学习是无效的，因为它不直接起作用。因此，我们提出了一种称为元对抗性学习的对比学习方法，该方法直接有助于实现元学习策略的目的。最后，我们通过实现明显的利润来建立一个新的最新网络。我们的方法带来了2.3、1.0、1.3、3.4和2.4 \％\％\％AP的改进，可在可可数据集上进行1-30张对象检测。我们的代码可在以下网址找到：https：//github.com/infinity7428/hanmcl

我们研究学习特征姿势的问题，即比例和方向，以构成感兴趣的图像区域。尽管它显然很简单，但问题是不平凡的。很难获得具有模型直接从中学习的明确姿势注释的大规模图像区域。为了解决这个问题，我们通过直方图对准技术提出了一个自制的学习框架。它通过随机重新缩放/旋转来生成成对的图像贴片，然后训练估计器以预测其比例/方向值，从而使其相对差异与所使用的重新分组/旋转一致。估算器学会了预测规模/方向的非参数直方图分布，而无需任何监督。实验表明，它在规模/方向估计中显着优于先前的方法，还可以通过将我们的斑块姿势纳入匹配过程中来改善图像匹配和6个DOF相机姿势估计。

在分析过度参数化神经网络的训练动力学方面的最新进展主要集中在广泛的网络上，因此无法充分解决深度在深度学习中的作用。在这项工作中，我们介绍了第一个无限深层但狭窄的神经网络的训练保证。我们研究具有特定初始化的多层感知器（MLP）的无限深度极限，并使用NTK理论建立了可训练性保证。然后，我们将分析扩展到无限深的卷积神经网络（CNN），并进行简短的实验。

在这项研究中，对两辆无人机（UAV）的室内路径计划进行了多机构增强学习。每个无人机都执行了从随机配对的初始位置到具有障碍的环境中的目标位置的尽快移动的任务。为了最大程度地减少训练时间并防止UAV的损害，通过模拟进行学习。考虑到多代理环境的非平稳特征，其中最佳行为会根据其他代理的作用而变化，因此其他无人机的作用也包括在每个无人机的状态空间中。课程学习是在两个阶段进行的，以提高学习效率。与获得73.6％和79.9％的其他学习策略相比，获得的目标率为89.0％。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

Cellular automata (CA) captivate researchers due to teh emergent, complex individualized behavior that simple global rules of interaction enact. Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. This new branch of CA is called neural cellular automata [1]. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines. We place many different convolutional neural networks in a grid. Each conv net cell outputs a prediction of what the next state will be, and minimizes predictive error. Cells received their neighbors' colors and fitnesses as input. Each cell's fitness score described how accurate its predictions were. Cells could also move to explore their environment and some stochasticity was applied to movement.

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.