IOT应用中的总是关于Tinyml的感知任务需要非常高的能量效率。模拟计算内存（CIM）使用非易失性存储器（NVM）承诺高效率，并提供自包含的片上模型存储。然而，模拟CIM推出了新的实际考虑因素，包括电导漂移，读/写噪声，固定的模数转换器增益等。必须解决这些附加约束，以实现可以通过可接受的模拟CIM部署的模型精度损失。这项工作描述了$ \ textit {analognets} $：tinyml模型用于关键字点（kws）和视觉唤醒词（VWW）的流行始终是on。模型架构专门为模拟CIM设计，我们详细介绍了一种全面的培训方法，以在推理时间内保持面对模拟非理想的精度和低精度数据转换器。我们还描述了AON-CIM，可编程，最小面积的相变存储器（PCM）模拟CIM加速器，具有新颖的层串行方法，以消除与完全流水线设计相关的复杂互连的成本。我们在校准的模拟器以及真正的硬件中评估了对校准模拟器的矛盾，并发现精度下降限制为KWS / VWW的PCM漂移（8位）24小时后的0.8 $ \％$ / 1.2 $ \％$。在14nm AON-CIM加速器上运行的analognets使用8位激活，分别使用8位激活，并增加到57.39 / 25.69个顶部/ w，以4美元$ 4 $ 57.39 / 25.69。

Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by stacking multiple layers. This paradigm suffers from two major limitations, over-squashing and poor long-range dependencies, that can be solved using global attention but significantly increases the computational cost to quadratic complexity. In this work, we propose an alternative approach to overcome these structural limitations by leveraging the ViT/MLP-Mixer architectures introduced in computer vision. We introduce a new class of GNNs, called Graph MLP-Mixer, that holds three key properties. First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on the Long Range Graph Benchmark (LRGB) and the TreeNeighbourMatch datasets. Second, they offer better speed and memory efficiency with a complexity linear to the number of nodes and edges, surpassing the related Graph Transformer and expressive GNN models. Third, they show high expressivity in terms of graph isomorphism as they can distinguish at least 3-WL non-isomorphic graphs. We test our architecture on 4 simulated datasets and 7 real-world benchmarks, and show highly competitive results on all of them.

This paper is devoted to the numerical resolution of McKean-Vlasov control problems via the class of mean-field neural networks introduced in our companion paper [25] in order to learn the solution on the Wasserstein space. We propose several algorithms either based on dynamic programming with control learning by policy or value iteration, or backward SDE from stochastic maximum principle with global or local loss functions. Extensive numerical results on different examples are presented to illustrate the accuracy of each of our eight algorithms. We discuss and compare the pros and cons of all the tested methods.

This paper describes the system developed at the Universitat Polit\`ecnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation techniques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers' baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings.

This paper studies the infinite-width limit of deep linear neural networks initialized with random parameters. We obtain that, when the number of neurons diverges, the training dynamics converge (in a precise sense) to the dynamics obtained from a gradient descent on an infinitely wide deterministic linear neural network. Moreover, even if the weights remain random, we get their precise law along the training dynamics, and prove a quantitative convergence result of the linear predictor in terms of the number of neurons. We finally study the continuous-time limit obtained for infinitely wide linear neural networks and show that the linear predictors of the neural network converge at an exponential rate to the minimal $\ell_2$-norm minimizer of the risk.

Recent advances in deep learning models for sequence classification have greatly improved their classification accuracy, specially when large training sets are available. However, several works have suggested that under some settings the predictions made by these models are poorly calibrated. In this work we study binary sequence classification problems and we look at model calibration from a different perspective by asking the question: Are deep learning models capable of learning the underlying target class distribution? We focus on sparse sequence classification, that is problems in which the target class is rare and compare three deep learning sequence classification models. We develop an evaluation that measures how well a classifier is learning the target class distribution. In addition, our evaluation disentangles good performance achieved by mere compression of the training sequences versus performance achieved by proper model generalization. Our results suggest that in this binary setting the deep-learning models are indeed able to learn the underlying class distribution in a non-trivial manner, i.e. by proper generalization beyond data compression.

The combination of machine learning models with physical models is a recent research path to learn robust data representations. In this paper, we introduce p$^3$VAE, a generative model that integrates a perfect physical model which partially explains the true underlying factors of variation in the data. To fully leverage our hybrid design, we propose a semi-supervised optimization procedure and an inference scheme that comes along meaningful uncertainty estimates. We apply p$^3$VAE to the semantic segmentation of high-resolution hyperspectral remote sensing images. Our experiments on a simulated data set demonstrated the benefits of our hybrid model against conventional machine learning models in terms of extrapolation capabilities and interpretability. In particular, we show that p$^3$VAE naturally has high disentanglement capabilities. Our code and data have been made publicly available at https://github.com/Romain3Ch216/p3VAE.

在过去的几年中，神经网络（NN）从实验室环境中发展为许多现实世界中的最新问题。结果表明，NN模型（即它们的重量和偏见）在训练过程中的重量空间中的独特轨迹上演变。随后，这种神经网络模型（称为模型动物园）的人群将在体重空间中形成结构。我们认为，这些结构的几何形状，曲率和平滑度包含有关训练状态的信息，并且可以揭示单个模型的潜在特性。使用这种模型动物园，可以研究（i）模型分析的新方法，（ii）发现未知的学习动力学，（iii）学习此类人群的丰富表示形式，或（iv）利用模型动物园来用于NN权重和NN权重的生成模型偏见。不幸的是，缺乏标准化模型动物园和可用的基准可以显着增加摩擦，以进一步研究NNS人群。通过这项工作，我们发布了一个新颖的模型动物园数据集，其中包含系统生成和多样化的NN模型种群，以进行进一步研究。总共提出的模型动物园数据集基于八个图像数据集，由27个模型动物园组成，该模型动物园训练有不同的超参数组合，包括50'360唯一的NN型号以及其稀疏双胞胎，导致超过3'844'360收集的型号。 。此外，对于模型动物园数据，我们提供了对动物园的深入分析，并为多个下游任务提供了基准。该数据集可在www.modelzoos.cc上找到。

给定模型动物园的神经网络权重的学习表示是一个新兴而具有挑战性的领域，从模型检查到神经体系结构搜索或知识蒸馏，具有许多潜在的应用。最近，在模型动物园进行训练的自动编码器能够学习一个超代理，该代表体捕获了动物园中模型的内在和外在特性。在这项工作中，我们扩展了超代表，以供生成使用以采样新的模型权重。我们提出的是层损失归一化，我们证明，这是基于超代表拓扑生成高性能模型和几种采样方法的关键。使用我们的方法生成的模型是多种多样的，性能的，并且能够超过强大的基准，从而在下游任务上进行了评估：初始化，合奏采样和传递学习。我们的结果表明，通过超代理通过过度代理，知识聚集从模型动物园到新模型的潜力，从而为新的研究方向铺平了途径。

从单个图像中恢复人头的几何形状，同时对材料和照明进行分解是一个严重不良的问题，需要事先解决。基于3D形态模型（3DMM）及其与可区分渲染器的组合的方法已显示出令人鼓舞的结果。但是，3DMM的表现力受到限制，它们通常会产生过度平滑和身份敏捷的3D形状，仅限于面部区域。最近，使用多层感知器参数化几何形状的神经场获得了高度准确的全头部重建。这些表示形式的多功能性也已被证明可有效解开几何形状，材料和照明。但是，这些方法需要几十个输入图像。在本文中，我们介绍了Sira，该方法从单个图像中，从一个图像中重建了具有高保真度几何形状和分解的灯光和表面材料的人头头像。我们的关键成分是基于神经场的两个数据驱动的统计模型，这些模型可以解决单视3D表面重建和外观分解的歧义。实验表明，Sira获得了最新的状态导致3D头重建，同时它成功地解开了全局照明以及弥漫性和镜面反照率。此外，我们的重建适合基于物理的外观编辑和头部模型重新构建。