全世界不可持续的捕鱼实践对海洋资源和生态系统构成了重大威胁。识别逃避监测系统的船只（称为“深色船只”）是管理和保护海洋环境健康的关键。随着基于卫星的合成孔径雷达（SAR）成像和现代机器学习（ML）的兴起，现在可以在全天候条件下白天或黑夜自动检测到黑暗的容器。但是，SAR图像需要特定于域的治疗，并且ML社区无法广泛使用。此外，对象（船只）是小而稀疏的，具有挑战性的传统计算机视觉方法。我们提出了用于训练ML模型的最大标记数据集，以检测和表征SAR的血管。 XView3-SAR由Sentinel-1任务中的近1,000张分析SAR图像组成，平均每个29,400 x-24,400像素。使用自动化和手动分析的组合对图像进行注释。每个SAR图像都伴随着共置的测深和风状射手。我们概述了XView3计算机视觉挑战的结果，这是一项国际竞争，使用XView3-SAR进行大规模的船舶检测和表征。我们发布数据（https://iuu.xview.us/）和代码（https://github.com/diux-xview），以支持该重要应用程序的ML方法的持续开发和评估。

Despite recent success in large language model (LLM) reasoning, LLMs still struggle with hierarchical multi-step reasoning like generating complex programs. In these cases, humans often start with a high-level algorithmic design and implement each part gradually. We introduce Parsel, a framework enabling automatic implementation and validation of complex algorithms with code LLMs, based on hierarchical function descriptions in natural language. Parsel can be used across domains requiring hierarchical reasoning, e.g. code synthesis, theorem proving, and robotic planning. We demonstrate Parsel's capabilities by using it to generate complex programs that cannot currently be automatically implemented from one description and backtranslating Python programs in the APPS dataset. Beyond modeling capabilities, Parsel allows problem-solving with high-level algorithmic designs, benefiting both students and professional programmers.

Language models have recently achieved strong performance across a wide range of NLP benchmarks. However, unlike benchmarks, real world tasks are often poorly specified, and agents must deduce the user's intended behavior from a combination of context, instructions, and examples. We investigate how both humans and models behave in the face of such task ambiguity by proposing AmbiBench, a new benchmark of six ambiguously-specified classification tasks. We evaluate humans and models on AmbiBench by seeing how well they identify the intended task using 1) instructions with varying degrees of ambiguity, and 2) different numbers of labeled examples. We find that the combination of model scaling (to 175B parameters) and training with human feedback data enables models to approach or exceed the accuracy of human participants across tasks, but that either one alone is not sufficient. In addition, we show how to dramatically improve the accuracy of language models trained without large-scale human feedback training by finetuning on a small number of ambiguous in-context examples, providing a promising direction for teaching models to generalize well in the face of ambiguity.

What role do augmentations play in contrastive learning? Recent work suggests that good augmentations are label-preserving with respect to a specific downstream task. We complicate this picture by showing that label-destroying augmentations can be useful in the foundation model setting, where the goal is to learn diverse, general-purpose representations for multiple downstream tasks. We perform contrastive learning experiments on a range of image and audio datasets with multiple downstream tasks (e.g. for digits superimposed on photographs, predicting the class of one vs. the other). We find that Viewmaker Networks, a recently proposed model for learning augmentations for contrastive learning, produce label-destroying augmentations that stochastically destroy features needed for different downstream tasks. These augmentations are interpretable (e.g. altering shapes, digits, or letters added to images) and surprisingly often result in better performance compared to expert-designed augmentations, despite not preserving label information. To support our empirical results, we theoretically analyze a simple contrastive learning setting with a linear model. In this setting, label-destroying augmentations are crucial for preventing one set of features from suppressing the learning of features useful for another downstream task. Our results highlight the need for analyzing the interaction between multiple downstream tasks when trying to explain the success of foundation models.

Semantic segmentation usually benefits from global contexts, fine localisation information, multi-scale features, etc. To advance Transformer-based segmenters with these aspects, we present a simple yet powerful semantic segmentation architecture, termed as IncepFormer. IncepFormer has two critical contributions as following. First, it introduces a novel pyramid structured Transformer encoder which harvests global context and fine localisation features simultaneously. These features are concatenated and fed into a convolution layer for final per-pixel prediction. Second, IncepFormer integrates an Inception-like architecture with depth-wise convolutions, and a light-weight feed-forward module in each self-attention layer, efficiently obtaining rich local multi-scale object features. Extensive experiments on five benchmarks show that our IncepFormer is superior to state-of-the-art methods in both accuracy and speed, e.g., 1) our IncepFormer-S achieves 47.7% mIoU on ADE20K which outperforms the existing best method by 1% while only costs half parameters and fewer FLOPs. 2) Our IncepFormer-B finally achieves 82.0% mIoU on Cityscapes dataset with 39.6M parameters. Code is available:github.com/shendu0321/IncepFormer.

Euclidean geometry is among the earliest forms of mathematical thinking. While the geometric primitives underlying its constructions, such as perfect lines and circles, do not often occur in the natural world, humans rarely struggle to perceive and reason with them. Will computer vision models trained on natural images show the same sensitivity to Euclidean geometry? Here we explore these questions by studying few-shot generalization in the universe of Euclidean geometry constructions. We introduce Geoclidean, a domain-specific language for Euclidean geometry, and use it to generate two datasets of geometric concept learning tasks for benchmarking generalization judgements of humans and machines. We find that humans are indeed sensitive to Euclidean geometry and generalize strongly from a few visual examples of a geometric concept. In contrast, low-level and high-level visual features from standard computer vision models pretrained on natural images do not support correct generalization. Thus Geoclidean represents a novel few-shot generalization benchmark for geometric concept learning, where the performance of humans and of AI models diverge. The Geoclidean framework and dataset are publicly available for download.

General mathematical reasoning is computationally undecidable, but humans routinely solve new problems. Moreover, discoveries developed over centuries are taught to subsequent generations quickly. What structure enables this, and how might that inform automated mathematical reasoning? We posit that central to both puzzles is the structure of procedural abstractions underlying mathematics. We explore this idea in a case study on 5 sections of beginning algebra on the Khan Academy platform. To define a computational foundation, we introduce Peano, a theorem-proving environment where the set of valid actions at any point is finite. We use Peano to formalize introductory algebra problems and axioms, obtaining well-defined search problems. We observe existing reinforcement learning methods for symbolic reasoning to be insufficient to solve harder problems. Adding the ability to induce reusable abstractions ("tactics") from its own solutions allows an agent to make steady progress, solving all problems. Furthermore, these abstractions induce an order to the problems, seen at random during training. The recovered order has significant agreement with the expert-designed Khan Academy curriculum, and second-generation agents trained on the recovered curriculum learn significantly faster. These results illustrate the synergistic role of abstractions and curricula in the cultural transmission of mathematics.

Modern speech enhancement (SE) networks typically implement noise suppression through time-frequency masking, latent representation masking, or discriminative signal prediction. In contrast, some recent works explore SE via generative speech synthesis, where the system's output is synthesized by a neural vocoder after an inherently lossy feature-denoising step. In this paper, we propose a denoising vocoder (DeVo) approach, where a vocoder accepts noisy representations and learns to directly synthesize clean speech. We leverage rich representations from self-supervised learning (SSL) speech models to discover relevant features. We conduct a candidate search across 15 potential SSL front-ends and subsequently train our vocoder adversarially with the best SSL configuration. Additionally, we demonstrate a causal version capable of running on streaming audio with 10ms latency and minimal performance degradation. Finally, we conduct both objective evaluations and subjective listening studies to show our system improves objective metrics and outperforms an existing state-of-the-art SE model subjectively.

经验丰富的用户通常在解决现实世界优化问题方面具有有用的知识和直觉。用户知识可以作为可变关系的配方，以帮助优化算法更快地找到良好的解决方案。此类间相互作用也可以自动从优化运行中的中间迭代中发现的高性能解决方案中自动学习 - 一种称为Innovization的过程。如果用户对这些关系进行审查，则可以在新生成的解决方案中执行，以将优化算法引导到搜索空间中实际上有希望的区域。对于大规模问题，这种可变关系的数量可能很高，就会出现挑战。本文提出了一个基于交互式知识的进化多目标优化（IK-EMO）框架，该框架将隐藏的可变关系提取为从不断发展的高性能解决方案中的知识，与用户共享它们以接收反馈，并将其应用于优化提高其有效性的过程。知识提取过程使用系统而优雅的图形分析方法，该方法与变量数量很好地缩放。在三个大规模的现实世界工程设计问题上证明了拟议的IK-EMO的工作。提出的知识提取过程和高性能解决方案的实现的简单性和优雅迅速表明了所提出的框架的力量。提出的结果应激发进一步的基于相互作用的优化研究，以实践其常规使用。

语言理解的概率模型是可解释和结构化的，例如隐喻理解的模型描述了有关潜在主题和特征的推论。但是，这些模型是为特定任务手动设计的。大型语言模型（LLMS）可以通过内在的学习来执行许多任务，但它们缺乏概率模型的清晰结构。在本文中，我们使用经过思考的提示将概率模型的结构引入LLMS。这些提示导致该模型推断潜在变量和有关其关系的理由，以选择隐喻的适当释义。所选择的潜在变量和关系是由认知心理学理解理论得出的。我们将这些提示应用于GPT-3的两个最大版本，并表明它们可以改善释义选择。