用于开放式对话的基于示例基础的生成模型，基于由猎犬提供的示例，利用生成模型和检索模型来产生响应。然而，它们经常忽略所检索的示例，同时产生响应或产生超过拟接到检索的示例的响应。在本文中，我们认为这些缺点是从开放域对话的一对多问题中衍生出来的。当检索的示例与与金响应显着不同的给定上下文相关时，基于示例的基础生成模型验证以忽略示例，因为示例对于产生金响应并不有用。另一方面，当检索到的示例性与金响应类似时，经过高度的，生成模型训练以依赖于示例。因此，我们提出了一种选择与金响应有语义相关的示例性的训练方法，而是从黄金响应的词汇偏移以减轻上述缺点。在培训阶段，我们建议的培训方法首先使用黄金响应而不是对话背景作为查询，以选择与金响应有关的语义相关的样本。然后，它消除了这种示例性，其中词汇类似于金响应，以减轻生成模型对该示例性的依赖性。剩余的示例可以与给定的上下文无关紧要，因为它们是根据金响应搜索的。因此，我们建议的培训方法进一步利用了给定的上下文与示例之间的相关评分，以惩罚不相关的样权。广泛的实验表明，我们所提出的培训方法减轻了现有的基于示例的生成模型的缺点，并显着提高了适当性和信息性方面的性能。

In this paper, we introduce an imagine network that can simulate itself through artificial association networks. Association, deduction, and memory networks are learned, and a network is created by combining the discriminator and reinforcement learning models. This model can learn various datasets or data samples generated in environments and generate new data samples.

我们介绍了记忆和记住任何数据的图形树内存网络。这个神经网络有两个回忆。一个由队列结构化的短期内存组成，可以解决类别不平衡问题和长期内存来存储对象的分发，引入存储和生成各种数据集的内容。

在本文中，我们介绍了图形树演绎网络，这是一种执行演绎推理的网络。为了产生新的关系和结果，需要高维思维，将各种公理和将结果放回另一个公理中，是必要的。例如，它会给两个命题：“苏格拉底是一个男人。”“所有人都是凡人。”两个命题可以用来推断出新的命题，“因此苏格拉底是凡人。”。为了评估，我们使用了Mnist DataSet，手写数值图像数据集，将其应用于组理论并显示执行演绎学习的结果。

在深度学习领域，已经开发了各种架构。然而，由于固定层结构，大多数研究限于特定的任务或数据集。本文不将信息提供作为网络模型的结构，而是作为称为关联树（AT）的数据结构。我们提出了两个人工协会网络（AAN），旨在通过分析人类神经网络的结构来解决现有网络的问题。定义单个图中的路径的起始点和结束点是困难的，并且树不能表达兄弟节点之间的关系。相反，AT可以表达叶子和根节点作为路径的起始点和兄弟节点之间的关系。根据树的结构而不是使用固定序列层，而不是使用固定序列图层为每个数据创建AT，并培训AAN。 AAN是数据驱动的学习，其中卷曲的数量根据树的深度而变化。此外，AAN可以通过递归学习同时学习各种类型的数据集。深度 - 第一卷积（DFC）将叶节点的交互结果以自下而上的方法对根节点进行编码到根节点，深度第一解码（DFD）将交互结果解码为自上而下的叶节点方法。我们进行了三个实验。第一个实验验证了是否可以通过组合AAN和特征提取网络来处理它。在第二，我们将网络的性能与单独学习的图像，声音和树，图形结构数据集进行了比较，通过连接这些网络同时学习的性能。在第三，我们验证了AAN的输出是否可以嵌入AT中的所有数据。因此，AATS学到了没有显着性能下降的情况。

我们提出了一种新颖的方法，即在强化学习框架中使用样式转移和对抗性学习的方式学习样式反应表示。在这里，样式是指任务核算的细节，例如图像中背景的颜色，在这种情况下，在具有不同样式的环境中概括学到的策略仍然是一个挑战。我们的方法着眼于学习样式不合时宜的表示，以固有的对抗性风格的发电机产生的不同图像样式训练演员，该样式在演员和发电机之间扮演最小游戏，而无需提供数据扩展的专家知识或其他类别的课程。对抗训练的标签。我们验证我们的方法比Procgen的最先进方法和分散控制套件的基准，并进一步研究从我们的模型中提取的功能，表明该模型更好地捕获不变性，并且不分散注意力，我们的方法可以实现竞争性或更好的性能。通过移动的风格。该代码可在https://github.com/postech-cvlab/style-agnostic-rl上找到。

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.

In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.

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.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.