量化浮点重量和深度卷积神经网络的激活到定点表示产生降低的存储器占用尺寸和推理时间。最近，努力已经进入零拍量量，不需要原始未标记的训练样本给定任务。这些最佳发布的作品依赖于学习批量归一化（BN）参数来推断出量化的激活范围。特别地，这些方法是基于经验估计框架或数据蒸馏方法而构建的，用于计算激活的范围。然而，当呈现不容纳BN层的网络时，这种方案的性能严重降低。在这一思路中，我们提出了广泛的零拍量化（GZSQ）框架，既不需要原始数据也不依赖于BN层统计。我们利用了数据蒸馏方法并仅利用模型的预先训练的重量来估计激活的范围校准的丰富数据。据我们所知，这是利用预制权重的分布以协助零射量量化的过程。拟议的计划显着优于现有的零点工程，例如，MobileNetv2的分类准确性的提高〜33％，以及各种任务的其他一些型号。我们还展示了拟议的工作跨多个开源量化框架的功效。重要的是，我们的作品是第一次尝试训练未来派零击中量化的零击中量化的深度神经网络。

Social recommender systems (SocialRS) simultaneously leverage user-to-item interactions as well as user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of Graph Neural Networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder, 2 groups of decoder, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize the benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions.

The adversarial input generation problem has become central in establishing the robustness and trustworthiness of deep neural nets, especially when they are used in safety-critical application domains such as autonomous vehicles and precision medicine. This is also practically challenging for multiple reasons-scalability is a common issue owing to large-sized networks, and the generated adversarial inputs often lack important qualities such as naturalness and output-impartiality. We relate this problem to the task of patching neural nets, i.e. applying small changes in some of the network$'$s weights so that the modified net satisfies a given property. Intuitively, a patch can be used to produce an adversarial input because the effect of changing the weights can also be brought about by changing the inputs instead. This work presents a novel technique to patch neural networks and an innovative approach of using it to produce perturbations of inputs which are adversarial for the original net. We note that the proposed solution is significantly more effective than the prior state-of-the-art techniques.

For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC). At the beginning of the 2000s, the RC paradigm appeared as a good option for employing recurrent NNs without dealing with the inconveniences of the training algorithms. RC models use a nonlinear dynamic system, with fixed recurrent neural network named the \textit{reservoir}, and learning process is restricted to adjusting a linear parametric function. %so the performance of learning is fast and precise. However, an RC model has several hyper-parameters, therefore EAs are helpful tools to figure out optimal RC architectures. We provide an overview of the results on the area, discuss novel advances, and we present our vision regarding the new trends and still open questions.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Cement is the most used construction material. The performance of cement hydrate depends on the constituent phases, viz. alite, belite, aluminate, and ferrites present in the cement clinker, both qualitatively and quantitatively. Traditionally, clinker phases are analyzed from optical images relying on a domain expert and simple image processing techniques. However, the non-uniformity of the images, variations in the geometry and size of the phases, and variabilities in the experimental approaches and imaging methods make it challenging to obtain the phases. Here, we present a machine learning (ML) approach to detect clinker microstructure phases automatically. To this extent, we create the first annotated dataset of cement clinker by segmenting alite and belite particles. Further, we use supervised ML methods to train models for identifying alite and belite regions. Specifically, we finetune the image detection and segmentation model Detectron-2 on the cement microstructure to develop a model for detecting the cement phases, namely, Cementron. We demonstrate that Cementron, trained only on literature data, works remarkably well on new images obtained from our experiments, demonstrating its generalizability. We make Cementron available for public use.

Running machine learning inference on tiny devices, known as TinyML, is an emerging research area. This task requires generating inference code that uses memory frugally, a task that standard ML frameworks are ill-suited for. A deployment framework for TinyML must be a) parametric in the number representation to take advantage of the emerging representations like posits, b) carefully assign high-precision to a few tensors so that most tensors can be kept in low-precision while still maintaining model accuracy, and c) avoid memory fragmentation. We describe MinUn, the first TinyML framework that holistically addresses these issues to generate efficient code for ARM microcontrollers (e.g., Arduino Uno, Due and STM32H747) that outperforms the prior TinyML frameworks.

我们分析和分类从电影评论构建的文本数据的观点。为此，我们使用量子机学习算法的基于内核的方法。为了组合量子内核，我们使用使用不同Pauli旋转门组合构造的电路，其中旋转参数是从文本数据获得的数据点的经典非线性函数。为了分析提出的模型的性能，我们使用决策树，增强分类器以及经典和量子支持向量机分析量子模型。我们的结果表明，就所有评估指标而言，量子内核模型或量子支持向量机优于用于分析的所有其他算法。与经典的支持向量机相比，量子支持向量机也会带来明显更好的结果，即使功能数量增加或尺寸增加。结果清楚地表明，如果功能的数量为$ 15 $，则使用量子支持向量机使用量子支持向量机的精度分数提高了$ 9.4 \％$，而经典支持向量机则将其提高。

ML-AS-A-Service继续增长，对非常强大的隐私保证的需求也在继续增长。安全推断已成为潜在的解决方案，其中加密原始图允许推理不向用户向用户揭示用户的输入或模型的权重。例如，模型提供商可以是一家诊断公司，该公司已经培训了一种最先进的Densenet-121模型来解释胸部X射线，并且用户可以在医院成为患者。尽管对于这种环境，确保推理原则上是可行的，但没有现有的技术使其大规模实用。 Cryptflow2框架提供了一种潜在的解决方案，其能力自动，正确地将清晰文本推理转换为安全模型的推断。但是，从Cryptflow2产生的安全推断在不切实际上很昂贵：在Densenet-121上解释单个X射线需要几乎3TB的通信。在本文中，我们解决了针对三项贡献的安全推断效率低下的重大挑战。首先，我们证明安全推理中的主要瓶颈是大型线性层，可以通过选择网络骨干的选择来优化，并使用用于有效的清晰文本推理开发的操作员。这一发现和强调与许多最近的作品偏离，这些作品着重于在执行较小网络的安全推断时优化非线性激活层。其次，基于对瓶颈卷积层的分析，我们设计了一个更有效的倒入替代品的X操作器。第三，我们表明，快速的Winograd卷积算法进一步提高了安全推断的效率。结合使用，这三个优化被证明对在CHEXPERT数据集中训练的X射线解释问题非常有效。

在其表示中，已经发现接受过文本数据训练的神经网络模型编码不希望的语言或敏感属性。删除此类属性是不平凡的，因为属性，文本输入和学习的表示之间存在复杂的关系。最近的工作提出了事后和对抗方法，以从模型的表示中删除此类不需要的属性。通过广泛的理论和经验分析，我们表明这些方法可以适得其反：它们无法完全删除属性，在最坏的情况下，最终可能会破坏所有与任务相关的功能。原因是方法对探测分类器的依赖作为属性的代理。即使在最有利的条件下，当属性在表示空间中的特征可以提供100％的学习探测分类器时，我们证明事后或对抗方法将无法正确删除属性。这些理论含义通过经验实验在合成，多NLI和Twitter数据集的模型上证实。对于敏感的属性去除（例如公平性），我们建议您谨慎使用这些方法，并提出伪造度量，以评估最终分类器的质量。