As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.
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In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties. Deep generative models are recently proposed to solve inverse problems, but these currently use expensive forward operators and struggle in precisely localizing the exact solutions and fully exploring the parameter spaces without missing solutions. In this work, we propose a novel approach (called iPage) to accelerate the inverse learning process by leveraging probabilistic inference from deep invertible models and deterministic optimization via fast gradient descent. Given a target property, the learned invertible model provides a posterior over the parameter space; we identify these posterior samples as an intelligent prior initialization which enables us to narrow down the search space. We then perform gradient descent to calibrate the inverse solutions within a local region. Meanwhile, a space-filling sampling is imposed on the latent space to better explore and capture all possible solutions. We evaluate our approach on three benchmark tasks and two created datasets with real-world applications from quantum chemistry and additive manufacturing, and find our method achieves superior performance compared to several state-of-the-art baseline methods. The iPage code is available at https://github.com/jxzhangjhu/MatDesINNe.
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Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching accuracy comparable to the original models. However, these tickets are proved to be notrobust to adversarial examples, and even worse than their PLM counterparts. To address this problem, we propose a novel method based on learning binary weight masks to identify robust tickets hidden in the original PLMs. Since the loss is not differentiable for the binary mask, we assign the hard concrete distribution to the masks and encourage their sparsity using a smoothing approximation of L0 regularization.Furthermore, we design an adversarial loss objective to guide the search for robust tickets and ensure that the tickets perform well bothin accuracy and robustness. Experimental results show the significant improvement of the proposed method over previous work on adversarial robustness evaluation.
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We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable Markov decision process (POMDP), and predictive state representation (PSR) as special cases. Toward finding the minimum assumption that empowers sample efficient learning, we propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation in online interactive decision making. In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs. Furthermore, in terms of algorithm design, we propose a generic posterior sampling algorithm, which can be implemented in both model-free and model-based fashion, under both fully observable and partially observable settings. The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.
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机器人武器广泛用于自动行业。但是,随着在机器人臂中深入学习的广泛应用,存在新的挑战,例如分配掌握计算能力和对安全性的需求不断增长。在这项工作中,我们提出了一种基于深度学习和边缘云协作的机器人手臂抓握方法。这种方法意识到了机器人组的任意掌握计划,并考虑了掌握效率和信息安全性。此外,由GAN训练的编码器和解码器使图像在压缩时可以加密,从而确保隐私的安全性。该模型在OCID数据集上达到92%的精度,图像压缩比达到0.03%,结构差值高于0.91。
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示范学习旨在通过在少数射击设置中提供回答的演示来指导及时的预测。尽管取得了令人鼓舞的结果,但现有工作仅将回答的示例与及时模板(包括原始上下文)相连,而无需任何其他操作,从而忽略了迅速示意的依赖性。此外,先前的研究发现,随机替换示威的标签极小地损害了性能,这表明该模型无法正确地了解示威活动所带来的知识。受到人类学习过程的启发,在本文中,我们引入了模仿演示学习(模仿),以通过明确模仿人类审查行为来加强演示学习,其中包括:(1)对比度学习机制,以专注于类似的演示。 (2)证明标签重新预测方法以合并已知知识。实验结果表明,我们提出的方法在14个分类中心中有11个实现了最先进的性能。进一步的研究还证明,模仿 - demo加强了迅速与示威之间的关联,这可以为探索示范学习的工作方式提供基础。
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学习不平衡是数据挖掘的基本挑战,在每个课程中,培训样本的比例不成比例。过度采样是通过为少数族裔生成合成样本来解决不平衡学习的有效技术。尽管已经提出了许多过采样算法,但它们在很大程度上依赖启发式方法,这可能是最佳选择的,因为我们可能需要针对不同数据集和基本分类器的不同采样策略,并且无法直接优化性能指标。在此激励的情况下,我们研究了开发一种基于学习的过采样算法以优化分类性能,这是一项艰巨的任务,因为庞大和等级的决策空间。在高水平上,我们需要确定要生成多少合成样品。在低级别,我们需要确定合成样品的位置,这取决于高级决策,因为样品的最佳位置在不同数量的样品中可能有所不同。为了应对挑战,我们提出了一种自动采样算法,可以共同优化不同级别的决策。由Smote〜 \ cite {Chawla2002smote}的成功的动机及其扩展,我们将生成过程作为Markov决策过程(MDP),由三个级别的策略组成,以在Smote搜索空间内生成合成样本。然后,我们利用深层的层次加强学习来优化验证数据的性能指标。在六个现实世界数据集上进行的广泛实验表明,自动变量极大地超过了最新的重新采样算法。该代码在https://github.com/daochenzha/autosmote上
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数据驱动的预测方法可以有效,准确地将蛋白质序列转化为生物活性结构,对于科学研究和治疗发展非常有价值。使用共同进化信息确定准确的折叠格局是现代蛋白质结构预测方法的成功基础。作为最新的状态,AlphaFold2显着提高了准确性,而无需进行明确的共同进化分析。然而,其性能仍然显示出对可用序列同源物的强烈依赖。我们研究了这种依赖性的原因,并提出了一种元生成模型Evogen,以弥补较差的MSA靶标的Alphafold2的表现不佳。 Evogen使我们能够通过降低搜索的MSA或生成虚拟MSA来操纵折叠景观,并帮助Alphafold2在低数据表方面准确地折叠,甚至通过单序预测来实现令人鼓舞的性能。能够用很少的MSA做出准确的预测,不仅可以更好地概括为孤儿序列的Alphafold2,而且使其在高通量应用程序中的使用民主化。此外,Evogen与AlphaFold2结合产生了一种概率结构生成方法,该方法可以探索蛋白质序列的替代构象,并且序列生成的任务意识可区分算法将使包括蛋白质设计在内的其他相关任务受益。
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本文旨在通过阅读敌人的思想(VM)来生成现实的人重新识别Reid的攻击样本。在本文中,我们提出了一种新颖的不起眼且可控的REID攻击基线LCYE,以生成对抗性查询图像。具体而言,LCYE首先通过模仿代理任务的教师学生记忆来提炼VM的知识。然后,这种知识的事先充当明确的密码,传达了VM所相信的必不可少和现实的内容,以实现准确的对抗性误导。此外,从LCYE的多个相反任务框架中受益,我们从对抗性攻击的角度进一步研究了REID模型的解释性和概括,包括跨域适应,跨模型共识和在线学习过程。对四个REID基准测试的广泛实验表明,我们的方法的表现优于其他最先进的攻击者,而白色框,黑框和目标攻击的边距很大。我们的代码现在可在https://gitfront.io/r/user-3704489/mkxusqdt4ffr/lcye/上找到。
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数据驱动的机器学习方法有可能显着加速材料设计的速率,而不是传统的人类指导方法。这些方法将有助于识别或在生成模型的情况下,甚至可以创建具有一组指定功能特性的新型材料结构,然后在实验室中合成或隔离。对于晶体结构的产生,关键的瓶颈在于为机器学习模型开发合适的原子结构指纹或表示,类似于分子生成中使用的基于图或微笑的表示。但是,找到对翻译,旋转和排列不变的数据有效表示,而笛卡尔原子坐标仍然是可逆的,仍然是一个持续的挑战。在这里,我们通过采用具有所需的不变的现有的不可糊化表示并开发算法来通过使用自动分化的基于梯度的优化来重建原子坐标,从而提出了一种替代方法。然后,可以将其与生成机器学习模型耦合,该模型在表示空间内生成新材料,而不是在数据范围内的笛卡尔空间中生成新材料。在这项工作中,我们使用以原子为中心的对称函数来实现这种端到端的结构生成方法,作为表示和条件变化自动编码器作为生成模型。我们能够成功地生成亚纳米PT纳米颗粒的新颖和有效的原子结构,作为概念证明。此外,该方法可以很容易地扩展到任何合适的结构表示形式,从而为基于结构的生成提供了强大的,可推广的框架。
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