无监督的对比度学习（UCL）是一种自我监督的学习技术，旨在通过将正面样本彼此接近，同时将负面样本推到嵌入空间中远处，以学习有用的表示功能。为了提高UCL的性能，几项作品引入了旨在选择“硬”阴性样本与UCL中使用的随机采样策略相比，旨在选择“硬”阴性样本的硬性阴性对比度学习（H-UCL）。在另一种方法中，在假设标签信息可用的假设下，有监督的对比学习（SCL）最近通过将UCL扩展到完全监督的环境来开发。在本文中，由于硬性采样策略在H-UCL中的有效性以及标签信息在SCL中的有用性的启发性，我们提出了一个称为硬性负责监督的对比度学习（H-SCL）的对比学习框架。我们的数值结果证明了H-SCL在几个图像数据集上对SCL和H-UCL的有效性。另外，从理论上讲，在某些条件下，H-SCL的目标函数可以受H-UCL的目标函数的界定，而不是由UCL的目标函数界定。因此，将H-UCL损失最小化可以作为最小化H-SCL损失的代理，而最小化UCL损失不能。正如我们数值表明H-SCL优于其他对比学习方法时，我们的理论结果（通过H-UCL损失界限H-SCL损失）有助于解释为什么H-UCL在实践中优于UCL。

本文考虑了Barycentric编码模型（BCM）下的测量估计问题，其中假定未知的度量属于有限的已知测量集的Wasserstein-2 Barycenters集合。估计该模型下的度量等同于估计未知的Barycentric坐标。我们为BCM下的测量估计提供了新颖的几何，统计和计算见解，由三个主要结果组成。我们的第一个主要结果利用了Wasserstein-2空间的Riemannian几何形状，以提供恢复Barycentric坐标的程序，作为假设对真实参考度量访问的二次优化问题的解决方案。基本的几何见解是，该二次问题的参数是由从给定度量到定义BCM的参考度量的最佳位移图之间的内部产物确定的。然后，我们的第二个主要结果建立了一种算法，用于求解BCM中坐标的算法，当时通过I.I.D进行经验观察到所有测量。样品。我们证明了该算法的精确收敛速率 - 取决于基本措施的平稳性及其维度 - 从而保证其统计一致性。最后，我们证明了BCM和相关估计程序在三个应用领域的实用性：（i）高斯措施的协方差估计； （ii）图像处理； （iii）自然语言处理。

我们研究了针对无监督对比代表学习的硬消耗采样分布设计的问题。我们分析了一种新的MIN-MAX框架，寻求一种表示最小化所有联轴器的最大（最差情况）的广义对比学习损失（正面和阴性样本之间的关节分布）并证明所得的最小最大值代表性将是堕落的。这提供了在联轴器上结合额外的正则化约束的第一理论典范。我们通过最佳运输理论的镜头重新解释最小最大问题，并利用正则化的传输联轴来控制负例的硬度。我们证明最近提出的最先进的硬负面采样分布是对应于耦合熵正则化的特殊情况。

本文通过添加解释性，可以显着提高对分类文件的监督嵌入的流行方法，即对比词媒体嵌入。通过将聚类促进机制结合到对比损失来实现这种可解释性。在几个公共数据集上，我们表明我们的方法在现有的基座上显着提高，同时通过识别一组关键字来解释群集，这些关键字是特定类的最具代表性的。我们的方法是有必要开发\ Texit的自然语言处理（NLP）方法{评估科学写作和思维的学生工作的新问题} - 这是一个（教育）学习科学领域的核心问题（LS）。在这种情况下，我们表明我们的方法会导致对来自生物阶层的实验室报告有意义的学生工作的有意义评估，并可以帮助LS研究人员进入学生理解和评估科学思想过程的证据。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

Reinforcement Learning (RL) is currently one of the most commonly used techniques for traffic signal control (TSC), which can adaptively adjusted traffic signal phase and duration according to real-time traffic data. However, a fully centralized RL approach is beset with difficulties in a multi-network scenario because of exponential growth in state-action space with increasing intersections. Multi-agent reinforcement learning (MARL) can overcome the high-dimension problem by employing the global control of each local RL agent, but it also brings new challenges, such as the failure of convergence caused by the non-stationary Markov Decision Process (MDP). In this paper, we introduce an off-policy nash deep Q-Network (OPNDQN) algorithm, which mitigates the weakness of both fully centralized and MARL approaches. The OPNDQN algorithm solves the problem that traditional algorithms cannot be used in large state-action space traffic models by utilizing a fictitious game approach at each iteration to find the nash equilibrium among neighboring intersections, from which no intersection has incentive to unilaterally deviate. One of main advantages of OPNDQN is to mitigate the non-stationarity of multi-agent Markov process because it considers the mutual influence among neighboring intersections by sharing their actions. On the other hand, for training a large traffic network, the convergence rate of OPNDQN is higher than that of existing MARL approaches because it does not incorporate all state information of each agent. We conduct an extensive experiments by using Simulation of Urban MObility simulator (SUMO), and show the dominant superiority of OPNDQN over several existing MARL approaches in terms of average queue length, episode training reward and average waiting time.