我们通过查看在弥漫表面上铸造的对象的阴影来研究个体的生物特征识别信息的问题。我们表明，通过最大似然分析，在代表性的情况下，阴影中的生物特征信息泄漏可以足够用于可靠的身份推断。然后，我们开发了一种基于学习的方法，该方法在实际设置中证明了这种现象，从而利用阴影中的微妙提示是泄漏的来源，而无需任何标记的真实数据。特别是，我们的方法依赖于构建由从每个身份的单个照片获得的3D面模型组成的合成场景。我们以完全无监督的方式将我们从合成数据中学到的知识转移到真实数据中。我们的模型能够很好地概括到真实的域，并且在场景中的几种变体都有坚固的范围。我们报告在具有未知几何形状和遮挡对象的场景中发生的身份分类任务中的高分类精度。

我们研究了数据驱动的深度学习方法的潜力，即从观察它们的混合物中分离两个通信信号。特别是，我们假设一个信号之一的生成过程（称为感兴趣的信号（SOI）），并且对第二个信号的生成过程不了解，称为干扰。单通道源分离问题的这种形式也称为干扰拒绝。我们表明，捕获高分辨率的时间结构（非平稳性），可以准确地同步与SOI和干扰，从而带来了可观的性能增长。有了这个关键的见解，我们提出了一种域信息神经网络（NN）设计，该设计能够改善“现成” NNS和经典检测和干扰拒绝方法，如我们的模拟中所示。我们的发现突出了特定于交流领域知识的关键作用在开发数据驱动的方法方面发挥了作用，这些方法具有前所未有的收益的希望。

我们研究了单通道源分离（SCSS）的问题，并专注于环化信号，这些信号特别适用于各种应用领域。与经典的SCSS方法不同，我们考虑了一个仅可用源的示例而不是模型的设置，从而激发了数据驱动的方法。对于具有基本环化高斯成分的源模型，我们为任何基于模型或数据驱动的分离方法建立了可达到的均方误差（MSE）的下限。我们的分析进一步揭示了最佳分离和相关实施挑战的操作。作为一种计算吸引力的替代方案，我们建议使用U-NET体系结构进行深度学习方法，该方法与最低MSE估计器具有竞争力。我们在模拟中证明，有了合适的域信息架构选择，我们的U-NET方法可以通过大幅减少的计算负担来达到最佳性能。

直接定位（DLOC）方法，该方法使用观察到的数据将源定位在一步过程中的未知位置，通常优于其间接的两步对应物（例如，使用到达的时间差异）。但是，水下声学DLOC方法需要对环境的先验知识，并且计算昂贵，因此很慢。我们建议，据我们所知，这是第一个数据驱动的DLOC方法。受经典和现代最佳模型的DLOC解决方案的启发，并利用了卷积神经网络（CNN）的功能，我们设计了一个基于CNN的整体解决方案。我们的方法包括专门量身定制的输入结构，体系结构，损失功能和渐进培训程序，在更广泛的机器学习背景下具有独立的兴趣。我们证明我们的方法优于有吸引力的替代方案，并且渐近地与基于Oracle的最佳模型解决方案的性能匹配。

With the advent of Neural Style Transfer (NST), stylizing an image has become quite popular. A convenient way for extending stylization techniques to videos is by applying them on a per-frame basis. However, such per-frame application usually lacks temporal-consistency expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal-consistency suffers from one or more of the following drawbacks. They (1) are only suitable for a limited range of stylization techniques, (2) can only be applied in an offline fashion requiring the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency-control. Note that existing consistent video-filtering approaches aim to completely remove flickering artifacts and thus do not respect any specific consistency-control aspect. For stylization tasks, however, consistency-control is an essential requirement where a certain amount of flickering can add to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that can stylize video streams while providing interactive consistency-control. Apart from stylization, our approach also supports various other image processing filters. For achieving interactive performance, we develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. We show that the final consistent video-output using our flow network is comparable to that being obtained using state-of-the-art optical-flow network. Further, we employ an adaptive combination of local and global consistent features and enable interactive selection between the two. By objective and subjective evaluation, we show that our method is superior to state-of-the-art approaches.

Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, for a computer to learn from data accurately and efficiently, some auxiliary information about the data distribution and target function should be provided to it through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. In this paper, we propose a learning model that exploits this multiscale data structure and discuss its statistical and computational benefits. The hierarchical learning model is inspired by the logical and progressive easy-to-hard learning mechanism of human beings and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.

Transformer language models (TLMs) are critical for most NLP tasks, but they are difficult to create for low-resource languages because of how much pretraining data they require. In this work, we investigate two techniques for training monolingual TLMs in a low-resource setting: greatly reducing TLM size, and complementing the masked language modeling objective with two linguistically rich supervised tasks (part-of-speech tagging and dependency parsing). Results from 7 diverse languages indicate that our model, MicroBERT, is able to produce marked improvements in downstream task evaluations relative to a typical monolingual TLM pretraining approach. Specifically, we find that monolingual MicroBERT models achieve gains of up to 18% for parser LAS and 11% for NER F1 compared to a multilingual baseline, mBERT, while having less than 1% of its parameter count. We conclude reducing TLM parameter count and using labeled data for pretraining low-resource TLMs can yield large quality benefits and in some cases produce models that outperform multilingual approaches.

Practical applications of mechanical metamaterials often involve solving inverse problems where the objective is to find the (multiple) microarchitectures that give rise to a given set of properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific sizes. One should, therefore, find multiple microarchitectural designs that exhibit the desired properties for a specimen with given dimensions. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture, meaning that peak stresses should be minimized as well. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, we propose a modular approach titled 'Deep-DRAM' that combines four decoupled models, including two deep learning models (DLM), a deep generative model (DGM) based on conditional variational autoencoders (CVAE), and direct finite element (FE) simulations. Deep-DRAM (deep learning for the design of random-network metamaterials) integrates these models into a unified framework capable of finding many solutions to the multi-objective inverse design problem posed here. The integrated framework first introduces the desired elastic properties to the DGM, which returns a set of candidate designs. The candidate designs, together with the target specimen dimensions are then passed to the DLM which predicts their actual elastic properties considering the specimen size. After a filtering step based on the closeness of the actual properties to the desired ones, the last step uses direct FE simulations to identify the designs with the minimum peak stresses.

Dual encoders are now the dominant architecture for dense retrieval. Yet, we have little understanding of how they represent text, and why this leads to good performance. In this work, we shed light on this question via distributions over the vocabulary. We propose to interpret the vector representations produced by dual encoders by projecting them into the model's vocabulary space. We show that the resulting distributions over vocabulary tokens are intuitive and contain rich semantic information. We find that this view can explain some of the failure cases of dense retrievers. For example, the inability of models to handle tail entities can be explained via a tendency of the token distributions to forget some of the tokens of those entities. We leverage this insight and propose a simple way to enrich query and passage representations with lexical information at inference time, and show that this significantly improves performance compared to the original model in out-of-domain settings.

Automatic differentiation (AD) is a technique for computing the derivative of a function represented by a program. This technique is considered as the de-facto standard for computing the differentiation in many machine learning and optimisation software tools. Despite the practicality of this technique, the performance of the differentiated programs, especially for functional languages and in the presence of vectors, is suboptimal. We present an AD system for a higher-order functional array-processing language. The core functional language underlying this system simultaneously supports both source-to-source forward-mode AD and global optimisations such as loop transformations. In combination, gradient computation with forward-mode AD can be as efficient as reverse mode, and the Jacobian matrices required for numerical algorithms such as Gauss-Newton and Levenberg-Marquardt can be efficiently computed.