自动化技术（例如人工智能（AI）和机器人技术）的快速进步构成了越来越多的职业自动化风险，可能会对劳动力市场产生重大影响。最近的社会经济研究表明，接下来的十年中，将近50％的职业处于自动化的高风险。但是，缺乏颗粒状数据和经验知情的模型限制了这些研究的准确性，并使预测哪些工作将是自动化的。在本文中，我们通过在自动化和非自动化职业之间执行分类任务来研究职业的自动化风险。可用信息是由标准职业分类（SOC）分类的910个职业的任务声明，技能和互动。要充分利用此信息，我们提出了一个基于图的半监督分类方法，名为\ textbf {a} utomated \ textbf {o} ccupation \ textbf {c}基于\ textbf {g} rassification \ textbf {n} etworks（\ textbf {aoc-gcn}）识别职业的自动化风险。该模型集成了一个异质图，以捕获职业的本地和全球环境。结果表明，我们提出的方法通过考虑职业的内部特征及其外部互动的信息来优于基线模型。这项研究可以帮助决策者在进入就业市场之前确定潜在的自动化职业并支持个人的决策。

在现实世界中，尽管对该领域的兴趣激增，但在稀疏回报协同环境下进行的加强学习仍然具有挑战性。先前的尝试表明，内在的奖励可以减轻稀疏引起的问题。在本文中，我们提出了一种新颖的固有奖励，该奖励受人类学习的启发，因为人类通过将当前的观察结果与历史知识进行比较来评估好奇心。具体而言，我们训练一个自我监督的预测模型，并保存一组模型参数的快照，而不会产生加法培训成本。然后，我们采用核规范来评估不同快照的预测之间的时间不一致，这可以进一步部署为内在的奖励。此外，提出了一种变异的加权机制，以自适应方式将权重分配给不同的快照。我们证明了所提出的方法在各种基准环境中的功效。结果表明，与其他基于奖励的方法相比，我们的方法可以提供压倒性的最先进性能，而不会产生额外的培训成本并保持更高的噪声耐受性。我们的代码将公开发布以提高可重复性。

外部奖励的稀疏性对加强学习（RL）构成了严重的挑战。当前，对好奇心已经做出了许多努力，这些努力可以为有效探索提供代表性的内在奖励。但是，挑战尚未得到解决。在本文中，我们提出了一种名为Dymecu的RL的好奇心，它代表了基于动态记忆的好奇心。受到人类好奇心和信息理论的启发，Dymecu由动态记忆和双重在线学习者组成。好奇心引起的话，如果记忆的信息无法处理当前状态，并且双重学习者之间的信息差距可以作为对代理的内在奖励进行表述，然后可以将这些状态信息巩固到动态内存中。与以前的好奇方法相比，dymecu可以更好地模仿人类的好奇心与动态记忆，并且可以根据双重学习者的引导范式动态地生长内存模块。在包括DeepMind Control Suite和Atari Suite在内的多个基准测试中，进行了大规模的经验实验，结果表明，Dymecu在有或没有外部奖励的情况下优于基于好奇心的方法。我们将发布代码以增强可重复性。

FreeSpace检测是自动驾驶技术的重要组成部分，并且在轨迹计划中起着重要作用。在过去的十年中，已证明基于深度学习的自由空间检测方法可行。但是，这些努力集中在城市道路环境上，由于缺乏越野基准，很少有针对越野自由空间检测专门设计的深度学习方法。在本文中，我们介绍了ORFD数据集，据我们所知，该数据集是第一个越野自由空间检测数据集。数据集收集在不同的场景（林地，农田，草地和乡村），不同的天气条件（阳光，多雨，雾气和雪地）以及不同的光线条件（明亮的光线，日光，暮光，黑暗）中，完全包含12,198 LIDAR点云和RGB图像对与可穿越的区域，不可传输区域和无法达到的区域进行了详细注释。我们提出了一个名为Off-NET的新型网络，该网络将变压器体系结构统一以汇总本地和全球信息，以满足大型接收领域的自由空间检测任务的要求。我们还向动态融合激光雷达和RGB图像信息提出了交叉注意，以进行准确的越野自由空间检测。数据集和代码可公开可用athttps：//github.com/chaytonmin/off-net。

作为在线广告和标记的关键组成部分，点击率（CTR）预测引起了行业和学术界领域的许多关注。最近，深度学习已成为CTR的主流方法论。尽管做出了可持续的努力，但现有的方法仍然构成了一些挑战。一方面，功能之间的高阶相互作用尚未探索。另一方面，高阶相互作用可能会忽略低阶字段的语义信息。在本文中，我们提出了一种名为Fint的新型预测方法，该方法采用了现场感知的交互层，该层捕获了高阶功能交互，同时保留了低阶现场信息。为了凭经验研究金融的有效性和鲁棒性，我们对三个现实数据库进行了广泛的实验：KDD2012，Criteo和Avazu。获得的结果表明，与现有方法相比，该五颗粒可以显着提高性能，而无需增加所需的计算量。此外，提出的方法通过A/B测试使大型在线视频应用程序的广告收入增加了约2.72 \％。为了更好地促进CTR领域的研究，我们发布了我们的代码以及参考实施，网址为：https：//github.com/zhishan01/fint。

Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current state-of-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The protocol as an algorithm for reproducing experiments using the KOWL-718 benchmark will be publicly released with code and containers at https://github.com/prijatelj/human-activity-recognition-in-an-open-world. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released.

As a neural network compression technique, post-training quantization (PTQ) transforms a pre-trained model into a quantized model using a lower-precision data type. However, the prediction accuracy will decrease because of the quantization noise, especially in extremely low-bit settings. How to determine the appropriate quantization parameters (e.g., scaling factors and rounding of weights) is the main problem facing now. Many existing methods determine the quantization parameters by minimizing the distance between features before and after quantization. Using this distance as the metric to optimize the quantization parameters only considers local information. We analyze the problem of minimizing local metrics and indicate that it would not result in optimal quantization parameters. Furthermore, the quantized model suffers from overfitting due to the small number of calibration samples in PTQ. In this paper, we propose PD-Quant to solve the problems. PD-Quant uses the information of differences between network prediction before and after quantization to determine the quantization parameters. To mitigate the overfitting problem, PD-Quant adjusts the distribution of activations in PTQ. Experiments show that PD-Quant leads to better quantization parameters and improves the prediction accuracy of quantized models, especially in low-bit settings. For example, PD-Quant pushes the accuracy of ResNet-18 up to 53.08% and RegNetX-600MF up to 40.92% in weight 2-bit activation 2-bit. The code will be released at https://github.com/hustvl/PD-Quant.

Most action recognition datasets and algorithms assume a closed world, where all test samples are instances of the known classes. In open set problems, test samples may be drawn from either known or unknown classes. Existing open set action recognition methods are typically based on extending closed set methods by adding post hoc analysis of classification scores or feature distances and do not capture the relations among all the video clip elements. Our approach uses the reconstruction error to determine the novelty of the video since unknown classes are harder to put back together and thus have a higher reconstruction error than videos from known classes. We refer to our solution to the open set action recognition problem as "Humpty Dumpty", due to its reconstruction abilities. Humpty Dumpty is a novel graph-based autoencoder that accounts for contextual and semantic relations among the clip pieces for improved reconstruction. A larger reconstruction error leads to an increased likelihood that the action can not be reconstructed, i.e., can not put Humpty Dumpty back together again, indicating that the action has never been seen before and is novel/unknown. Extensive experiments are performed on two publicly available action recognition datasets including HMDB-51 and UCF-101, showing the state-of-the-art performance for open set action recognition.

Existing natural language understanding (NLU) models often rely on dataset biases rather than intended task-relevant features to achieve high performance on specific datasets. As a result, these models perform poorly on datasets outside the training distribution. Some recent studies address the above issue by reducing the weights of biased samples during the training process. However, these methods still encode biased latent features in representations and neglect the dynamic nature of bias, which hinders model prediction. We propose an NLU debiasing method, named debiasing contrastive learning (DCT), to simultaneously alleviate the above problems based on contrastive learning. We devise a debiasing positive sampling strategy to mitigate biased latent features by selecting the least similar biased positive samples. We also propose a dynamic negative sampling strategy to capture the dynamic influence of biases by employing a bias-only model to dynamically select the most similar biased negative samples. We conduct experiments on three NLU benchmark datasets. Experimental results show that DCT outperforms state-of-the-art baselines on out-of-distribution datasets while maintaining in-distribution performance. We also verify that DCT can reduce biased latent features from the model's representations.

Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.