物理知识的神经网络（PINN）已成为解决各种域中的部分微分方程（PDE）的强大工具。尽管PINNS的先前研究主要集中在训练期间构建和平衡损失功能以避免最小值，但采样搭配点对PINNS性能的影响很大程度上被忽略了。在这项工作中，我们发现PINN的性能可以随着不同的采样策略而显着变化，并且使用固定的搭配点可能对PINNS与正确解决方案的收敛性很小。特别是（1）我们假设对PINN的培训依赖于从初始和/或边界条件点到内部点的成功“传播”，而采样策略差的PINN可能会卡在琐事的解决方案上，如果有\ textit {传播失败}。 （2）我们证明，传播失败的特征是高度不平衡的PDE残留场，在非常狭窄的区域中观察到非常高的残留物。 （3）为了减轻传播失败，我们提出了一种新颖的\ textit {Evolutionary采样}（EVO）方法，该方法可以逐步积累高PDE残差区域中的搭配点。我们进一步提供EVO的扩展，以尊重因果关系原理，同时解决时间依赖性PDE。我们从经验上证明了我们提出的方法在各种PDE问题中的功效和效率。

许多操作数值天气预报系统中使用的数据同化程序基于4D-VAR算法的变体。解决4D-VAR问题的成本是由物理模型的前进和伴随评估的成本为主。这通过快速，近似代理模型来激励他们的替代。神经网络为代理模型的数据驱动创建提供了一个有希望的方法。已显示代理4D-VAR问题解决方案的准确性，明确地依赖于对其他代理建模方法和一般非线性设置的准确建模和伴随的准确建模。我们制定和分析若干方法，将衍生信息纳入神经网络替代品的构建。通过训练集数据和Lorenz-63系统上的顺序数据同化设置来测试生成的网络。与没有伴随信息的替代网络培训的代理网络相比，两种方法表现出卓越的性能，显示将伴随信息纳入训练过程的益处。

物理引导的神经网络（PGNNS）代表了使用物理引导（PG）丢失功能（捕获具有已知物理学中的网络输出中的违规）培训的新出现类的神经网络，以及数据中包含的监督。 PGNN中的现有工作表明，使用恒定的折衷参数，在神经网络目标中添加单个PG损耗功能的功效，以确保更好的普遍性。然而，在具有竞争梯度方向的多个PG函数的存在中，需要自适应地调谐在训练过程中不同的PG损耗功能的贡献，以获得更广泛的解决方案。我们展示了在求解基于物理学的特征值方程的最低（或最高）特征向量的通用神经网络问题中竞争PG损失的存在，这在许多科学问题中通常遇到。我们提出了一种新的方法来处理竞争PG损失，并在量子力学和电磁繁殖中的两个激励应用中展示其在学习普遍解决方案中的功效。这项工作中使用的所有代码和数据都可以在https://github.com/jayroxis/cophy-pgnn获得。

Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data. We observed two shortcomings: illogical synthetic SQL queries from independent column sampling and arbitrary table joins. To address these issues, we propose a novel synthesis framework that incorporates key relationships from schema, imposes strong typing, and conducts schema-distance-weighted column sampling. We also adopt an intermediate representation (IR) for the SQL-to-text task to further improve the quality of the generated natural language questions. When existing powerful semantic parsers are pre-finetuned on our high-quality synthesized data, our experiments show that these models have significant accuracy boosts on popular benchmarks, including new state-of-the-art performance on Spider.

The availability of challenging benchmarks has played a key role in the recent progress of machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi-Agent Challenge (SMAC) has become a popular testbed for centralised training with decentralised execution. However, after years of sustained improvement on SMAC, algorithms now achieve near-perfect performance. In this work, we conduct new analysis demonstrating that SMAC is not sufficiently stochastic to require complex closed-loop policies. In particular, we show that an open-loop policy conditioned only on the timestep can achieve non-trivial win rates for many SMAC scenarios. To address this limitation, we introduce SMACv2, a new version of the benchmark where scenarios are procedurally generated and require agents to generalise to previously unseen settings (from the same distribution) during evaluation. We show that these changes ensure the benchmark requires the use of closed-loop policies. We evaluate state-of-the-art algorithms on SMACv2 and show that it presents significant challenges not present in the original benchmark. Our analysis illustrates that SMACv2 addresses the discovered deficiencies of SMAC and can help benchmark the next generation of MARL methods. Videos of training are available at https://sites.google.com/view/smacv2

A reliable critic is central to on-policy actor-critic learning. But it becomes challenging to learn a reliable critic in a multi-agent sparse reward scenario due to two factors: 1) The joint action space grows exponentially with the number of agents 2) This, combined with the reward sparseness and environment noise, leads to large sample requirements for accurate learning. We show that regularising the critic with spectral normalization (SN) enables it to learn more robustly, even in multi-agent on-policy sparse reward scenarios. Our experiments show that the regularised critic is quickly able to learn from the sparse rewarding experience in the complex SMAC and RWARE domains. These findings highlight the importance of regularisation in the critic for stable learning.

Automatic speech recognition research focuses on training and evaluating on static datasets. Yet, as speech models are increasingly deployed on personal devices, such models encounter user-specific distributional shifts. To simulate this real-world scenario, we introduce LibriContinual, a continual learning benchmark for speaker-specific domain adaptation derived from LibriVox audiobooks, with data corresponding to 118 individual speakers and 6 train splits per speaker of different sizes. Additionally, current speech recognition models and continual learning algorithms are not optimized to be compute-efficient. We adapt a general-purpose training algorithm NetAug for ASR and create a novel Conformer variant called the DisConformer (Disentangled Conformer). This algorithm produces ASR models consisting of a frozen 'core' network for general-purpose use and several tunable 'augment' networks for speaker-specific tuning. Using such models, we propose a novel compute-efficient continual learning algorithm called DisentangledCL. Our experiments show that the DisConformer models significantly outperform baselines on general ASR i.e. LibriSpeech (15.58% rel. WER on test-other). On speaker-specific LibriContinual they significantly outperform trainable-parameter-matched baselines (by 20.65% rel. WER on test) and even match fully finetuned baselines in some settings.

For the majority of the machine learning community, the expensive nature of collecting high-quality human-annotated data and the inability to efficiently finetune very large state-of-the-art pretrained models on limited compute are major bottlenecks for building models for new tasks. We propose a zero-shot simple approach for one such task, Video Moment Retrieval (VMR), that does not perform any additional finetuning and simply repurposes off-the-shelf models trained on other tasks. Our three-step approach consists of moment proposal, moment-query matching and postprocessing, all using only off-the-shelf models. On the QVHighlights benchmark for VMR, we vastly improve performance of previous zero-shot approaches by at least 2.5x on all metrics and reduce the gap between zero-shot and state-of-the-art supervised by over 74%. Further, we also show that our zero-shot approach beats non-pretrained supervised models on the Recall metrics and comes very close on mAP metrics; and that it also performs better than the best pretrained supervised model on shorter moments. Finally, we ablate and analyze our results and propose interesting future directions.

Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning. Yet, they fail miserably on the recently proposed Winoground dataset, which challenges models to match paired images and English captions, with items constructed to overlap lexically but differ in meaning (e.g., "there is a mug in some grass" vs. "there is some grass in a mug"). By annotating the dataset using new fine-grained tags, we show that solving the Winoground task requires not just compositional language understanding, but a host of other abilities like commonsense reasoning or locating small, out-of-focus objects in low-resolution images. In this paper, we identify the dataset's main challenges through a suite of experiments on related tasks (probing task, image retrieval task), data augmentation, and manual inspection of the dataset. Our analysis suggests that a main challenge in visuolinguistic models may lie in fusing visual and textual representations, rather than in compositional language understanding. We release our annotation and code at https://github.com/ajd12342/why-winoground-hard .

手写的文本识别问题是由计算机视觉社区的研究人员广泛研究的，因为它的改进和适用于日常生活的范围，它是模式识别的子域。自从过去几十年以来，基于神经网络的系统的计算能力提高了计算能力，因此有助于提供最新的手写文本识别器。在同一方向上，我们采用了两个最先进的神经网络系统，并将注意力机制合并在一起。注意技术已被广泛用于神经机器翻译和自动语音识别的领域，现在正在文本识别域中实现。在这项研究中，我们能够在IAM数据集上达到4.15％的字符错误率和9.72％的单词错误率，7.07％的字符错误率和GW数据集的16.14％单词错误率与现有的Flor合并后，GW数据集的单词错误率等。建筑学。为了进一步分析，我们还使用了类似于Shi等人的系统。具有贪婪解码器的神经网络系统，观察到基本模型的字符错误率提高了23.27％。