我们提出了一个自动静态分析仪Pytea，可检测Pytorch码中的张量误差。张量误差在深度神经网络代码中是至关重要的;一旦在训练阶段中间发生张量形状不匹配，就会丢失大部分训练成本和中间结果。鉴于输入Pytorch源，Pytea静态跟踪每个可能的执行路径，收集路径的张量操作序列所需的张量形状约束，并决定如果约束是不匹配的（因此发生形状误差）。 Pytea的可扩展性和精确铰链对现实世界的Pytorch应用的特点：Pytea保守修剪后的执行路径数很少爆炸，循环简单，以通过我们的符号抽象来限制。我们测试了Pytea在官方Pytorch存储库中的项目中，以及在StackOverflow中发现的一些张计错误代码。 Pytea在几秒钟内成功地检测这些代码中的张量形状误差。

Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide high-contrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However, it has been shown that using a single perceptual loss is insufficient for accurately restoring locally varying diverse shapes in images, often generating undesirable artifacts or unnatural details. For this reason, combinations of various losses, such as perceptual, adversarial, and distortion losses, have been attempted, yet it remains challenging to find optimal combinations. Hence, in this paper, we propose a new SISR framework that applies optimal objectives for each region to generate plausible results in overall areas of high-resolution outputs. Specifically, the framework comprises two models: a predictive model that infers an optimal objective map for a given low-resolution (LR) input and a generative model that applies a target objective map to produce the corresponding SR output. The generative model is trained over our proposed objective trajectory representing a set of essential objectives, which enables the single network to learn various SR results corresponding to combined losses on the trajectory. The predictive model is trained using pairs of LR images and corresponding optimal objective maps searched from the objective trajectory. Experimental results on five benchmarks show that the proposed method outperforms state-of-the-art perception-driven SR methods in LPIPS, DISTS, PSNR, and SSIM metrics. The visual results also demonstrate the superiority of our method in perception-oriented reconstruction. The code and models are available at https://github.com/seungho-snu/SROOE.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

最近的研究通过卷积神经网络（CNNS）显着提高了单图像超分辨率（SR）的性能。虽然可以有许多用于给定输入的高分辨率（HR）解决方案，但大多数现有的基于CNN的方法在推理期间不会探索替代解决方案。获得替代SR结果的典型方法是培训具有不同丢失权重的多个SR模型，并利用这些模型的组合。我们通过利用多任务学习，我们提出了一种更有效的方法来培训单个可调SR模型的单一可调SR模型。具体地，我们在训练期间优化具有条件目标的SR模型，其中目标是不同特征级别的多个感知损失的加权之和。权重根据给定条件而变化，并且该组重量被定义为样式控制器。此外，我们提出了一种适用于该训练方案的架构，该架构是配备有空间特征变换层的残留残余密集块。在推理阶段，我们培训的模型可以在样式控制地图上生成局部不同的输出。广泛的实验表明，所提出的SR模型在没有伪影的情况下产生各种所需的重建，并对最先进的SR方法产生相当的定量性能。

最近的生成模型的成功表明，利用多模态嵌入空间可以使用文本信息操纵图像。然而，由于源的动态特性，使用其他来源而不是声音的文本来操纵图像，而不是声音，并不容易。特别是，声音可以传达真实世界的生动情感和动态表达。在这里，我们提出了一个框架，该框架将声音直接编码为多模态（图像文本）嵌入空间，并从空间操纵图像。我们的音频编码器受过培训以产生来自音频输入的潜在表示，该音频输入被强制与多模式嵌入空间中的图像和文本表示对齐。我们使用基于对齐的嵌入式的直接潜在优化方法进行声音引导图像操纵。我们还表明，我们的方法可以混合文本和音频模态，这丰富了各种图像修改。我们验证了定量和定性的声音引导图像操纵的有效性。我们还表明，我们的方法可以混合不同的模态，即文本和音频，这丰富了图像修改的各种。零射频分类和语义级图像分类的实验表明，我们所提出的模型优于其他文本和声音引导最先进的方法。

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.

We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.

Learning to predict masked tokens in a sequence has been shown to be a powerful pretraining objective for large-scale language models. After training, such masked language models can provide distributions of tokens conditioned on bidirectional context. In this short draft, we show that such bidirectional conditionals often demonstrate considerable inconsistencies, i.e., they can not be derived from a coherent joint distribution when considered together. We empirically quantify such inconsistencies in the simple scenario of bigrams for two common styles of masked language models: T5-style and BERT-style. For example, we show that T5 models often confuse its own preference regarding two similar bigrams. Such inconsistencies may represent a theoretical pitfall for the research work on sampling sequences based on the bidirectional conditionals learned by BERT-style MLMs. This phenomenon also means that T5-style MLMs capable of infilling will generate discrepant results depending on how much masking is given, which may represent a particular trust issue.