Knowledge of the symmetries of reinforcement learning (RL) systems can be used to create compressed and semantically meaningful representations of a low-level state space. We present a method of automatically detecting RL symmetries directly from raw trajectory data without requiring active control of the system. Our method generates candidate symmetries and trains a recurrent neural network (RNN) to discriminate between the original trajectories and the transformed trajectories for each candidate symmetry. The RNN discriminator's accuracy for each candidate reveals how symmetric the system is under that transformation. This information can be used to create high-level representations that are invariant to all symmetries on a dataset level and to communicate properties of the RL behavior to users. We show in experiments on two simulated RL use cases (a pusher robot and a UAV flying in wind) that our method can determine the symmetries underlying both the environment physics and the trained RL policy.
translated by 谷歌翻译
We identify the task of measuring data to quantitatively characterize the composition of machine learning data and datasets. Similar to an object's height, width, and volume, data measurements quantify different attributes of data along common dimensions that support comparison. Several lines of research have proposed what we refer to as measurements, with differing terminology; we bring some of this work together, particularly in fields of computer vision and language, and build from it to motivate measuring data as a critical component of responsible AI development. Measuring data aids in systematically building and analyzing machine learning (ML) data towards specific goals and gaining better control of what modern ML systems will learn. We conclude with a discussion of the many avenues of future work, the limitations of data measurements, and how to leverage these measurement approaches in research and practice.
translated by 谷歌翻译
Semi-supervised learning methods can train high-accuracy machine learning models with a fraction of the labeled training samples required for traditional supervised learning. Such methods do not typically involve close review of the unlabeled training samples, making them tempting targets for data poisoning attacks. In this paper we investigate the vulnerabilities of semi-supervised learning methods to backdoor data poisoning attacks on the unlabeled samples. We show that simple poisoning attacks that influence the distribution of the poisoned samples' predicted labels are highly effective - achieving an average attack success rate as high as 96.9%. We introduce a generalized attack framework targeting semi-supervised learning methods to better understand and exploit their limitations and to motivate future defense strategies.
translated by 谷歌翻译
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.
translated by 谷歌翻译
我们从算法上确定了规范多面体复合物的所有维度的区域和方面，这是relu网络分解其输入空间的通用对象。我们表明，规范多面体复合物的顶点的位置及其相对于层地图的迹象决定了所有维度的整个刻面结构。我们提出了一种计算完整的组合结构的算法，利用了我们的定理，该定理与规范多面体复合物的双重复合物是立方体的，并且具有与其相结构兼容的乘法。所得算法在中间神经元的数量上是数值稳定的多项式时间，并且在所有维度上都获得了准确的信息。这使我们能够获得例如具有低维输入的网络的决策边界的真实拓扑。我们在初始化时在此类网络上运行经验，发现单独的宽度不会增加观察到的拓扑，而是在深度存在下的宽度。我们的算法的源代码可在https://github.com/mmasden/caronicalpoly上在线访问。
translated by 谷歌翻译
我们提出了一种微调方法，可以改善从单个图像重建的3D几何形状的外观。我们利用单眼深度估计的进步来获得差异图，并提出了一种新颖的方法，可以通过求解相关摄像机参数的优化，将2D归一化差异图转换为3D点云，在从差异中创建3D点云后，我们引入了一种方法来引入一种方法将新点云与现有信息结合在一起，形成更忠实，更详细的最终几何形状。我们通过在合成图像和真实图像上进行多个实验证明了方法的功效。
translated by 谷歌翻译
了解细胞类型的多样性及其在大脑中的功能是神经科学中的关键挑战之一。大规模数据集的出现引起了细胞类型分类的不偏不倚和定量方法。我们提出了GraphDino，一种学习神经元3D形态的低尺寸表示的纯粹数据驱动方法。 GraphDino是一种新的图形表示，用于在变压器模型上利用自我监督学习的空间图表。它在节点与经典图卷积处理之间的注意力全局交互之间平滑地插值。我们表明，该方法能够屈服于与基于手动特征的分类相当的形态细胞型聚类，并且对两种不同物种和皮质区域的专家标记的细胞类型表示良好的对应关系。我们的方法适用于在数据集中的样本是图形和图形级嵌入的设置中的神经科学中。
translated by 谷歌翻译