Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.

自动驾驶汽车和自主驾驶研究一直受到现代人工智能应用中主要有希望的前景。根据先进的驾驶员辅助系统（ADAS）的演变，自动驾驶车辆和自主驱动系统的设计变得复杂和安全至关重要。通常，智能系统同时和有效地激活ADAS功能。因此，必须考虑可靠的ADAS功能协调，安全地控制驱动系统。为了处理这个问题，本文提出了一种随机的对抗性模仿学习（RAIL）算法。铁路是一种新的无衍生仿制学习方法，用于具有各种ADAS功能协调的自主驾驶;因此，它模仿决策者的运作，可以使用各种ADAS功能控制自动驾驶。该方法能够培训涉及激光雷达数据的决策者，并控制多车道复合道环境中的自主驾驶。基于仿真的评估验证了所提出的方法实现了所需的性能。

预计未来几十年的全球粮食不安全将加速气候变化率和人口迅速增加。在这种静脉中，重要的是在每种饮食生产水平上消除效率低下。最近深入学习的进步可以帮助降低这种效率低下，但他们的申请尚未成为整个行业的主流，以大规模的规模诱导经济成本。为此，已将现代技术（如CNNS（卷积神经网络）应用于RPQD（原始产生质量检测）任务。另一方面，变压器在其他方式中的视野中的成功首次亮相使我们能够在RPQD中预计这些基于变压器的模型更好的性能。在这项工作中，我们专门调查了最近的最先进的水流（移位的Windows）变压器，这些变压器可以在窗口和窗口间的方式中计算自我关注。我们将Swin变压器与CNN模型进行比较四个RPQD图像数据集，每个CNN模型都包含不同种类的生成：水果和蔬菜，鱼类，猪肉和牛肉。我们观察到Swin Transformer不仅实现了更好或更有竞争力的性能，而且还具有数据和计算效率，使其成为现实世界的实际部署的理想选择。据我们所知，这是第一个对RPQD任务的大规模实证研究，我们希望在未来的作品中更加关注。

数据稀缺和噪声是机器学习工业应用中的重要问题。然而，设计可扩展和广义的方法往往挑战，以解决具有黑盒式模型的数据集的基本分布和语义特性。因此，以数据为中心的方法对于机器学习操作管道的自动化至关重要。为了充当这种自动化的基础，我们建议一个用于改进图像分类问题中数据质量的域名不可知的管道。该管道包含数据估值，清洁和增强。通过这些方法的适当组合，我们只能在数据中心AI竞争中达到84.711％的测试精度（最荣誉在最具创新性中提及）。

我们介绍并分析新的一阶优化算法系列，它概括并统一镜像血统和双平均。在该系列的框架内，我们定义了用于约束优化的新算法，这些算法结合了镜像血统和双平均的优点。我们的初步仿真研究表明，这些新算法在某些情况下显着优于可用方法。

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

Several leading methods on public benchmarks for depth-from-stereo rely on memory-demanding 4D cost volumes and computationally intensive 3D convolutions for feature matching. We suggest a new way to process the 4D cost volume where we merge two different concepts in one deeply integrated framework to achieve a symbiotic relationship. A feature matching part is responsible for identifying matching pixels pairs along the baseline while a concurrent image volume part is inspired by depth-from-mono CNNs. However, instead of predicting depth directly from image features, it provides additional context to resolve ambiguities during pixel matching. More technically, the processing of the 4D cost volume is separated into a 2D propagation and a 3D propagation part. Starting from feature maps of the left image, the 2D propagation assists the 3D propagation part of the cost volume at different layers by adding visual features to the geometric context. By combining both parts, we can safely reduce the scale of 3D convolution layers in the matching part without sacrificing accuracy. Experiments demonstrate that our end-to-end trained CNN is ranked 2nd on KITTI2012 and ETH3D benchmarks while being significantly faster than the 1st-ranked method. Furthermore, we notice that the coupling of image and matching-volume improves fine-scale details as demonstrated by our qualitative analysis.

Uniform-precision neural network quantization has gained popularity since it simplifies densely packed arithmetic unit for high computing capability. However, it ignores heterogeneous sensitivity to the impact of quantization errors across the layers, resulting in sub-optimal inference accuracy. This work proposes a novel neural architecture search called neural channel expansion that adjusts the network structure to alleviate accuracy degradation from ultra-low uniform-precision quantization. The proposed method selectively expands channels for the quantization sensitive layers while satisfying hardware constraints (e.g., FLOPs, PARAMs). Based on in-depth analysis and experiments, we demonstrate that the proposed method can adapt several popular networks channels to achieve superior 2-bit quantization accuracy on CIFAR10 and ImageNet. In particular, we achieve the best-to-date Top-1/Top-5 accuracy for 2-bit ResNet50 with smaller FLOPs and the parameter size.

Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning. However, the ICL performance does not scale well with the number of available training samples as it is limited by the inherent input length constraint of the underlying language model. Meanwhile, many studies have revealed that language models are also powerful feature extractors, allowing them to be utilized in a black-box manner and enabling the linear probing paradigm, where lightweight discriminators are trained on top of the pre-extracted input representations. This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. PALP inherits the scalability of linear probing and the capability of enforcing language models to derive more meaningful representations via tailoring input into a more conceivable form. Throughout in-depth investigations on various datasets, we verified that PALP significantly enhances the input representations closing the gap between ICL in the data-hungry scenario and fine-tuning in the data-abundant scenario with little training overhead, potentially making PALP a strong alternative in a black-box scenario.

Cartoonization is a task that renders natural photos into cartoon styles. Previous deep cartoonization methods only have focused on end-to-end translation, which may hinder editability. Instead, we propose a novel solution with editing features of texture and color based on the cartoon creation process. To do that, we design a model architecture to have separate decoders, texture and color, to decouple these attributes. In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures. We also introduce an HSV color augmentation to induce the networks to generate diverse and controllable color translation. To the best of our knowledge, our work is the first deep approach to control the cartoonization at inference while showing profound quality improvement over to baselines.