Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.
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We introduce an end-to-end computational framework that enables hyperparameter optimization with the DeepHyper library, accelerated training, and interpretable AI inference with a suite of state-of-the-art AI models, including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-Net. We use these AI models and the benchmark QM9, hMOF, and MD17 datasets to showcase the prediction of user-specified materials properties in modern computing environments, and to demonstrate translational applications for the modeling of small molecules, crystals and metal organic frameworks with a unified, stand-alone framework. We deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership class computing environments.
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Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.
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We consider private federated learning (FL), where a server aggregates differentially private gradient updates from a large number of clients in order to train a machine learning model. The main challenge is balancing privacy with both classification accuracy of the learned model as well as the amount of communication between the clients and server. In this work, we build on a recently proposed method for communication-efficient private FL -- the MVU mechanism -- by introducing a new interpolation mechanism that can accommodate a more efficient privacy analysis. The result is the new Interpolated MVU mechanism that provides SOTA results on communication-efficient private FL on a variety of datasets.
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Many modern computer vision algorithms suffer from two major bottlenecks: scarcity of data and learning new tasks incrementally. While training the model with new batches of data the model looses it's ability to classify the previous data judiciously which is termed as catastrophic forgetting. Conventional methods have tried to mitigate catastrophic forgetting of the previously learned data while the training at the current session has been compromised. The state-of-the-art generative replay based approaches use complicated structures such as generative adversarial network (GAN) to deal with catastrophic forgetting. Additionally, training a GAN with few samples may lead to instability. In this work, we present a novel method to deal with these two major hurdles. Our method identifies a better embedding space with an improved contrasting loss to make classification more robust. Moreover, our approach is able to retain previously acquired knowledge in the embedding space even when trained with new classes. We update previous session class prototypes while training in such a way that it is able to represent the true class mean. This is of prime importance as our classification rule is based on the nearest class mean classification strategy. We have demonstrated our results by showing that the embedding space remains intact after training the model with new classes. We showed that our method preformed better than the existing state-of-the-art algorithms in terms of accuracy across different sessions.
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Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. NP techniques can interface with symbolic domain knowledge from scientists, such as prior knowledge and experimental context, to produce interpretable outputs. We identify opportunities and challenges between current NP models and scientific workflows, with real-world examples from behavior analysis in science: to enable the use of NP broadly for workflows across the natural and social sciences.
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在对关键安全环境的强化学习中,通常希望代理在所有时间点(包括培训期间)服从安全性限制。我们提出了一种称为Spice的新型神经符号方法,以解决这个安全的探索问题。与现有工具相比,Spice使用基于符号最弱的先决条件的在线屏蔽层获得更精确的安全性分析,而不会不适当地影响培训过程。我们在连续控制基准的套件上评估了该方法,并表明它可以达到与现有的安全学习技术相当的性能,同时遭受较少的安全性违规行为。此外,我们提出的理论结果表明,在合理假设下,香料会收敛到最佳安全政策。
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检测和避免(DAA)功能对于无人飞机系统(UAS)的安全操作至关重要。本文介绍了Airtrack,这是一个仅实时视觉检测和跟踪框架,尊重SUAS系统的大小,重量和功率(交换)约束。鉴于遥远飞机的低信噪比(SNR),我们建议在深度学习框架中使用完整的分辨率图像,以对齐连续的图像以消除自我动态。然后,对齐的图像在级联的初级和次级分类器中下游使用,以改善多个指标的检测和跟踪性能。我们表明,Airtrack在亚马逊机载对象跟踪(AOT)数据集上胜过最先进的基线。多次现实世界的飞行测试与CESSNA 172与通用航空交通相互作用,并在受控的设置中朝着UAS飞向UAS的其他近碰撞飞行测试,该拟议方法满足了新引入的ASTM F3442/F3442M标准DAA标准。经验评估表明,我们的系统的概率超过900m,范围超过95%。视频可在https://youtu.be/h3ll_wjxjpw上找到。
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在本文中,我们描述了使用汉密尔顿蒙特卡洛方法从基于经验可能性的后验进行采样的{\ tt r}软件包。基于经验可能性的方法论已在最近的许多感兴趣问题的贝叶斯建模中使用。该半摩擦过程可以轻松地将非参数分布估计器的灵活性与参数模型的可解释性结合在一起。该模型是通过估计基于方程的约束来指定的。从贝叶斯的经验可能性(贝耶斯)后部提取推断是具有挑战性的。可能性是数值计算的,因此不存在后部的闭合表达。此外,对于任何有限尺寸的样本,可能性的支持是非凸,这阻碍了许多马尔可夫链蒙特卡洛(MCMC)程序的快速混合。最近已经表明,使用对数经验可能性梯度的性质,可以设计有效的汉密尔顿蒙特卡洛(HMC)算法来从贝内斯尔后部采样。该软件包要求用户仅指定估计方程,先验及其各自的梯度。从参数后部绘制的MCMC样本,并获得了用户所需的各种细节。
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最近,经验可能性已在贝叶斯框架下广泛应用。马尔可夫链蒙特卡洛(MCMC)方法经常用于从感兴趣参数的后验分布中采样。然而,可能性支持的复杂性,尤其是非凸性的性质,在选择适当的MCMC算法时建立了巨大的障碍。这种困难限制了在许多应用中基于贝叶斯的经验可能性(贝叶赛)方法的使用。在本文中,我们提出了一个两步的大都会黑斯廷斯算法,以从贝耶斯后期进行采样。我们的建议是在层次上指定的,其中确定经验可能性的估计方程用于根据其余参数的建议值提出一组参数的值。此外,我们使用经验可能性讨论贝叶斯模型的选择,并将我们的两步大都会黑斯廷斯算法扩展到可逆的跳跃马尔可夫链蒙特卡洛手术程序,以便从最终的后验中采样。最后,提出了我们提出的方法的几种应用。
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