本文提出了一种增强学习（RL）框架，该框架利用Frank-Wolfe策略优化来解决利益区域（ROI）内部框架内编码的编码-Tree-Unit（CTU）位分配。大多数以前的基于RL的方法采用了单批评家设计，其中失真最小化和速率正则化的奖励是通过经验选择的超参数加权的。最近，提出了双批评设计，以通过交替的速度和失真批评者来更新演员。但是，它的收敛不能保证。为了解决这些问题，我们介绍了神经弗兰克 - 沃尔夫政策优化（NFWPO），以将CTU级分配作为动作约束的RL问题进行制定。在这个新框架中，我们利用费率评论家来预测一套可行的行动。借助这套可行的集合，援引失真的评论家来更新演员，以最大程度地提高ROI加权图像质量受速率约束。用X265产生的实验结果证实了所提出的方法比其他基线的优越性。

这项工作介绍了称为B-CANF的B帧编码框架，该框架利用有条件的增强标准化流量来进行B框架编码。学到的B框架编码的探索较少，更具挑战性。B-CANF是由有条件的P框架编码的最新进展的动机，是将基于流的模型应用于条件运动和框架间编码的首次尝试。B-CANF功能帧型自适应编码，该编码可以学习层次B框架编码更好的位分配。B-Canf还引入了一种特殊类型的B帧，称为B*-Frame，以模拟P框架编码。在常用数据集上，B-CANF达到了最新的压缩性能，在随机访问配置下显示了与HM-16.23相当的BD速率结果（在PSNR-RGB方面）。

本文基于条件增强归一化流（ANF），介绍了一种基于端到端的学习视频压缩系统，称为CANF-VC。大多数博学的视频压缩系统采用与传统编解码器相同的基于混合的编码体系结构。关于条件编码的最新研究表明，基于混合的编码的亚地区，并为深层生成模型打开了在创建新编码框架中发挥关键作用的机会。 CANF-VC代表了一种新的尝试，该尝试利用条件ANF学习有条件框架间编码的视频生成模型。我们之所以选择ANF，是因为它是一种特殊类型的生成模型，其中包括各种自动编码器作为一种特殊情况，并且能够获得更好的表现力。 CANF-VC还将条件编码的想法扩展到运动编码，形成纯粹的条件编码框架。对常用数据集的广泛实验结果证实了CANF-VC对最新方法的优越性。

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

Robust Markov decision processes (RMDPs) are promising models that provide reliable policies under ambiguities in model parameters. As opposed to nominal Markov decision processes (MDPs), however, the state-of-the-art solution methods for RMDPs are limited to value-based methods, such as value iteration and policy iteration. This paper proposes Double-Loop Robust Policy Gradient (DRPG), the first generic policy gradient method for RMDPs with a global convergence guarantee in tabular problems. Unlike value-based methods, DRPG does not rely on dynamic programming techniques. In particular, the inner-loop robust policy evaluation problem is solved via projected gradient descent. Finally, our experimental results demonstrate the performance of our algorithm and verify our theoretical guarantees.

Language models (LMs) have demonstrated remarkable performance on downstream tasks, using in-context exemplars or human instructions. Recent works have shown that chain-of-thought (CoT) prompting can elicit models to solve complex reasoning tasks, step-by-step. However, the efficacy of prompt-based CoT methods is restricted to very large LMs such as GPT-3 (175B), thus limiting deployability. In this paper, we revisit the fine-tuning approach to enable complex reasoning in smaller LMs, optimized to efficiently perform a specific task. We propose Fine-tune-CoT, a method that leverages the capabilities of very large LMs to generate reasoning samples and teach smaller models via fine-tuning. We evaluate our method on publicly available LMs across a wide range of complex tasks and model sizes. We find that Fine-tune-CoT enables substantial reasoning capability in small models, whereas previous prompt-based baselines exhibit near-random performance. Student models can even outperform the teacher in some tasks while reducing model size requirements by several orders of magnitude. We conduct extensive ablations and sample studies to understand the reasoning capabilities of student models. We also identify several important nuances that have been overlooked in concurrent fine-tuning works on CoT and address them in our analysis.

Semi-supervised object detection is important for 3D scene understanding because obtaining large-scale 3D bounding box annotations on point clouds is time-consuming and labor-intensive. Existing semi-supervised methods usually employ teacher-student knowledge distillation together with an augmentation strategy to leverage unlabeled point clouds. However, these methods adopt global augmentation with scene-level transformations and hence are sub-optimal for instance-level object detection. In this work, we propose an object-level point augmentor (OPA) that performs local transformations for semi-supervised 3D object detection. In this way, the resultant augmentor is derived to emphasize object instances rather than irrelevant backgrounds, making the augmented data more useful for object detector training. Extensive experiments on the ScanNet and SUN RGB-D datasets show that the proposed OPA performs favorably against the state-of-the-art methods under various experimental settings. The source code will be available at https://github.com/nomiaro/OPA.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.