Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed in recent years to overcome the adversarial attacks. In this paper, we propose a multi-head uncertainty inference (MH-UI) framework for detecting adversarial attack examples. We adopt a multi-head architecture with multiple prediction heads (i.e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI. Using independent heads at different depths, the normalized predictions are assumed to follow the same Dirichlet distribution, and we estimate distribution parameter of it by moment matching. Cognitive uncertainty brought by the adversarial attacks will be reflected and amplified on the distribution. Experimental results show that the proposed MH-UI framework can outperform all the referred UI methods in the adversarial attack detection task with different settings.

最近，社区对模型缩放的关注越来越多，并有助于开发具有广泛尺度的模型家族。当前的方法要么简单地采用单发NAS的方式来构建非结构性和不可缩放的模型家族，要么依靠手动固定的缩放策略来扩展不必要的最佳基础模型。在本文中，我们桥接了两个组件，并将Scalenet提出到共同搜索基础模型和缩放策略，以便缩放大型模型可以具有更有希望的性能。具体来说，我们设计了一个超级植物，以体现具有不同尺寸频谱（例如拖鞋）的模型。然后，可以通过基于马尔可夫链的进化算法与基本模型进行交互学习缩放策略，并概括以开发更大的模型。为了获得一个体面的超级植物，我们设计了一种分层抽样策略，以增强其训练充足并减轻干扰。实验结果表明，我们的缩放网络在各种失败的方面都具有显着的性能优势，但搜索成本至少降低了2.53倍。代码可在https://github.com/luminolx/scalenet上找到。

视觉变形金刚（VITS）继承了NLP的成功，但它们的结构尚未充分调查并针对视觉任务进行优化。最简单的解决方案之一是通过CNN中的广泛使用的神经结构搜索（NAS）直接搜索最佳的问题。但是，我们经验探讨了这种直接的适应将遇到灾难性的失败，并对超级形式的培训感到沮丧。在本文中，我们认为，由于VITS主要在令牌嵌入具有很小的归纳偏差上运行，因此不同架构的通道的不平衡将使重量共享假设恶化并导致培训不稳定。因此，我们开发了一种新的循环重量共享机制，用于令牌的VITS嵌入式，这使得每个通道能够更均匀地贡献所有候选架构。此外，我们还提出了身份转移，以减轻超级形式的多对一问题，并利用弱的增强和正规化技术以维持更稳定的培训。基于这些，我们所提出的方法Vitas在Deit-and Twins的Vits中取得了显着的优势。例如，只有1.4美元的G拖鞋预算，我们搜索的架构有3.3 \％$ ImageNet-比基准Deit为1美元$ k准确性。我们的结果达到3.0美元，我们的结果达到了82.0 \％$ 1 $ k，$ 1 $ k，$ 45.9 \％$ 2017 $上涨，这是2.4美元的$ 2.4 \％$优于其他VITS。

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.

Most semantic communication systems leverage deep learning models to provide end-to-end transmission performance surpassing the established source and channel coding approaches. While, so far, research has mainly focused on architecture and model improvements, but such a model trained over a full dataset and ergodic channel responses is unlikely to be optimal for every test instance. Due to limitations on the model capacity and imperfect optimization and generalization, such learned models will be suboptimal especially when the testing data distribution or channel response is different from that in the training phase, as is likely to be the case in practice. To tackle this, in this paper, we propose a novel semantic communication paradigm by leveraging the deep learning model's overfitting property. Our model can for instance be updated after deployment, which can further lead to substantial gains in terms of the transmission rate-distortion (RD) performance. This new system is named adaptive semantic communication (ASC). In our ASC system, the ingredients of wireless transmitted stream include both the semantic representations of source data and the adapted decoder model parameters. Specifically, we take the overfitting concept to the extreme, proposing a series of ingenious methods to adapt the semantic codec or representations to an individual data or channel state instance. The whole ASC system design is formulated as an optimization problem whose goal is to minimize the loss function that is a tripartite tradeoff among the data rate, model rate, and distortion terms. The experiments (including user study) verify the effectiveness and efficiency of our ASC system. Notably, the substantial gain of our overfitted coding paradigm can catalyze semantic communication upgrading to a new era.

在鸟眼中学习强大的表现（BEV），以进行感知任务，这是趋势和吸引行业和学术界的广泛关注。大多数自动驾驶算法的常规方法在正面或透视视图中执行检测，细分，跟踪等。随着传感器配置变得越来越复杂，从不同的传感器中集成了多源信息，并在统一视图中代表功能至关重要。 BEV感知继承了几个优势，因为代表BEV中的周围场景是直观和融合友好的。对于BEV中的代表对象，对于随后的模块，如计划和/或控制是最可取的。 BEV感知的核心问题在于（a）如何通过从透视视图到BEV来通过视图转换来重建丢失的3D信息； （b）如何在BEV网格中获取地面真理注释； （c）如何制定管道以合并来自不同来源和视图的特征； （d）如何适应和概括算法作为传感器配置在不同情况下各不相同。在这项调查中，我们回顾了有关BEV感知的最新工作，并对不同解决方案进行了深入的分析。此外，还描述了该行业的BEV方法的几种系统设计。此外，我们推出了一套完整的实用指南，以提高BEV感知任务的性能，包括相机，激光雷达和融合输入。最后，我们指出了该领域的未来研究指示。我们希望该报告能阐明社区，并鼓励对BEV感知的更多研究。我们保留一个活跃的存储库来收集最新的工作，并在https://github.com/openperceptionx/bevperception-survey-recipe上提供一包技巧的工具箱。

经典的交流范式专注于准确地通过嘈杂的渠道传输位，而香农理论则对可靠通信速率提供了基本的理论限制。在这种方法中，位平均对待，并且通信系统忽略了这些位传达或如何使用的含义。可以预见的是，对智力和简洁性的未来沟通将发挥主导作用，连接的智能代理的扩散需要对编码传输范式进行根本性的重新思考，以支持地平线上的新通信形态。最近的“语义通信”概念提供了有希望的研究方向。将语义指南注入编码传输设计以实现语义感知通信，这表现出了进一步突破性和可靠性的巨大潜力。本文阐明了语义引导的源和频道编码作为语义通信的传输范式，该传输范式可以利用数据语义的多样性和无线通道多样性，以增强整个系统性能。我们介绍一般的系统体系结构和关键技术，并指出有关此主题的一些开放问题。

在本文中，我们提出了一类新的高效的深源通道编码方法，可以在非线性变换下的源分布下，可以在名称非线性变换源通道编码（NTSCC）下收集。在所考虑的模型中，发射器首先了解非线性分析变换以将源数据映射到潜伏空间中，然后通过深关节源通道编码将潜在的表示发送到接收器。我们的模型在有效提取源语义特征并提供源通道编码的侧面信息之前，我们的模型包括强度。与现有的传统深度联合源通道编码方法不同，所提出的NTSCC基本上学习源潜像和熵模型，作为先前的潜在表示。因此，开发了新的自适应速率传输和高辅助辅助编解码器改进机制以升级深关节源通道编码。整个系统设计被制定为优化问题，其目标是最小化建立感知质量指标下的端到端传输率失真性能。在简单的示例源和测试图像源上，我们发现所提出的NTSCC传输方法通常优于使用标准的深关节源通道编码和基于经典分离的数字传输的模拟传输。值得注意的是，由于其剧烈的内容感知能力，所提出的NTSCC方法可能会支持未来的语义通信。

Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.

A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint function values and derivatives are available, but only stochastic approximations of the objective function and its associated derivatives can be computed via inexact probabilistic zeroth- and first-order oracles. Under reasonable assumptions, a high-probability bound on the iteration complexity of the algorithm to approximate first-order stationarity is derived. Numerical results on standard nonlinear optimization test problems illustrate the advantages and limitations of our proposed method.