多代理系统（例如自动驾驶或工厂）作为服务的一些最相关的应用程序显示混合动机方案，代理商可能具有相互矛盾的目标。在这些环境中，代理可能会在独立学习下的合作方面学习不良的结果，例如过度贪婪的行为。在现实世界社会的动机中，在这项工作中，我们建议利用市场力量为代理商成为合作的激励措施。正如囚犯困境的迭代版本所证明的那样，拟议的市场配方可以改变游戏的动力，以始终如一地学习合作政策。此外，我们在空间和时间扩展的设置中评估了不同数量的代理的方法。我们从经验上发现，市场的存在可以通过其交易活动改善总体结果和代理人的回报。

加固学习在机器学习中推动了令人印象深刻的进步。同时，量子增强机学习算法使用量子退火的底层划伤。最近，已经提出了一种组合两个范例的多代理强化学习（MARL）架构。这种新的算法利用Q值近似的量子Boltzmann机器（QBMS）在收敛所需的时间步长方面具有优于常规的深度增强学习。但是，该算法仅限于单代理和小型2x2多代理网格域。在这项工作中，我们提出了对原始概念的延伸，以解决更具挑战性问题。类似于Classic DQN，我们添加了重播缓冲区的体验，并使用不同的网络来估计目标和策略值。实验结果表明，学习变得更加稳定，使代理能够在具有更高复杂性的网格域中找到最佳策略。此外，我们还评估参数共享如何影响多代理域中的代理行为。量子采样证明是一种有希望的加强学习任务的方法，但目前受到QPU尺寸的限制，因此通过输入和Boltzmann机器的大小。

由于昂贵的挖掘程序，光纤到-UTH（FTTH）网络的扩展会产生高成本。因此，优化规划过程，最大限度地减少地球挖掘工作的成本导致大量节省。在数学上，FTTH网络问题可以被描述为最小的Steiner树问题。尽管在过去的几十年中已经在集中进行了密集地进行了强烈调查了施泰纳的问题，但可以在新的计算范例和新兴方法的帮助下进一步优化。这项工作研究即将到来的技术，例如Quantum退火，模拟退火和自然启发方法，如进化算法或基于粘液模具的优化。此外，我们还调查分区和简化方法。在几个现实生活中评估，我们可以在大多数域上表达传统的广泛使用的基线（NetworkX近似求解器）。先前分区初始图和所呈现的基于粘液模具的方法对于成本有效的近似特别有价值。 Quantum退火似乎很有希望，但受到可用Qubits的数量的限制。

One of the major challenges of machine translation (MT) is ambiguity, which can in some cases be resolved by accompanying context such as an image. However, recent work in multimodal MT (MMT) has shown that obtaining improvements from images is challenging, limited not only by the difficulty of building effective cross-modal representations but also by the lack of specific evaluation and training data. We present a new MMT approach based on a strong text-only MT model, which uses neural adapters and a novel guided self-attention mechanism and which is jointly trained on both visual masking and MMT. We also release CoMMuTE, a Contrastive Multilingual Multimodal Translation Evaluation dataset, composed of ambiguous sentences and their possible translations, accompanied by disambiguating images corresponding to each translation. Our approach obtains competitive results over strong text-only models on standard English-to-French benchmarks and outperforms these baselines and state-of-the-art MMT systems with a large margin on our contrastive test set.

Language models (LMs) have demonstrated remarkable performance on downstream tasks, using in-context exemplars or human instructions. Recent works have shown that chain-of-thought (CoT) prompting can elicit models to solve complex reasoning tasks, step-by-step. However, the efficacy of prompt-based CoT methods is restricted to very large LMs such as GPT-3 (175B), thus limiting deployability. In this paper, we revisit the fine-tuning approach to enable complex reasoning in smaller LMs, optimized to efficiently perform a specific task. We propose Fine-tune-CoT, a method that leverages the capabilities of very large LMs to generate reasoning samples and teach smaller models via fine-tuning. We evaluate our method on publicly available LMs across a wide range of complex tasks and model sizes. We find that Fine-tune-CoT enables substantial reasoning capability in small models, whereas previous prompt-based baselines exhibit near-random performance. Student models can even outperform the teacher in some tasks while reducing model size requirements by several orders of magnitude. We conduct extensive ablations and sample studies to understand the reasoning capabilities of student models. We also identify several important nuances that have been overlooked in concurrent fine-tuning works on CoT and address them in our analysis.

Multilingual Pretrained Language Models (MPLMs) have shown their strong multilinguality in recent empirical cross-lingual transfer studies. In this paper, we propose the Prompts Augmented by Retrieval Crosslingually (PARC) pipeline to improve the zero-shot performance on low-resource languages (LRLs) by augmenting the context with semantically similar sentences retrieved from a high-resource language (HRL) as prompts. PARC improves the zero-shot performance on three downstream tasks (binary sentiment classification, topic categorization and natural language inference) with multilingual parallel test sets across 10 LRLs covering 6 language families in both unlabeled settings (+5.1%) and labeled settings (+16.3%). PARC-labeled also outperforms the finetuning baseline by 3.7%. We find a significant positive correlation between cross-lingual transfer performance on one side, and the similarity between the high- and low-resource languages as well as the amount of low-resource pretraining data on the other side. A robustness analysis suggests that PARC has the potential to achieve even stronger performance with more powerful MPLMs.

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning.

Can we leverage the audiovisual information already present in video to improve self-supervised representation learning? To answer this question, we study various pretraining architectures and objectives within the masked autoencoding framework, motivated by the success of similar methods in natural language and image understanding. We show that we can achieve significant improvements on audiovisual downstream classification tasks, surpassing the state-of-the-art on VGGSound and AudioSet. Furthermore, we can leverage our audiovisual pretraining scheme for multiple unimodal downstream tasks using a single audiovisual pretrained model. We additionally demonstrate the transferability of our representations, achieving state-of-the-art audiovisual results on Epic Kitchens without pretraining specifically for this dataset.

Pixel-level labels are particularly expensive to acquire. Hence, pretraining is a critical step to improve models on a task like semantic segmentation. However, prominent algorithms for pretraining neural networks use image-level objectives, e.g. image classification, image-text alignment a la CLIP, or self-supervised contrastive learning. These objectives do not model spatial information, which might be suboptimal when finetuning on downstream tasks with spatial reasoning. In this work, we propose to pretrain networks for semantic segmentation by predicting the relative location of image parts. We formulate this task as a classification problem where each patch in a query view has to predict its position relatively to another reference view. We control the difficulty of the task by masking a subset of the reference patch features visible to those of the query. Our experiments show that this location-aware (LOCA) self-supervised pretraining leads to representations that transfer competitively to several challenging semantic segmentation benchmarks.

Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale datasets such as AudioSet. However, Transformers are demanding in terms of model size and computational requirements compared to CNNs. We propose a training procedure for efficient CNNs based on offline Knowledge Distillation (KD) from high-performing yet complex transformers. The proposed training schema and the efficient CNN design based on MobileNetV3 results in models outperforming previous solutions in terms of parameter and computational efficiency and prediction performance. We provide models of different complexity levels, scaling from low-complexity models up to a new state-of-the-art performance of .483 mAP on AudioSet. Source Code available at: https://github.com/fschmid56/EfficientAT