Hierarchical Reinforcement Learning (HRL) is promising to tackle the long-term sparse reward problem. However, goal conditioned HRL, which decomposes the goal into a series of sub-goals, suffers from sub-goal search i...
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Medical image anomaly detection refers to machine learning techniques to analyze and identify lesions and abnormalities in them. However, in medical images, anomaly samples are usually sparse, which can lead to superv...
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Modern neural networks are known to give overconfident predictions for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outlier dataset to regularize the model ...
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Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sh...
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Developing large,soft grippers with high omnidirectional load(above 40 kg)has always been *** address this challenge by developing a powerful soft gripper that can grasp the human body based on a soft-enclosed graspin...
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Developing large,soft grippers with high omnidirectional load(above 40 kg)has always been *** address this challenge by developing a powerful soft gripper that can grasp the human body based on a soft-enclosed grasping structure and a soft-rigid coupling *** envelope size of the proposed soft gripper is 611.6 mm×559 mm×490.7 mm,the maximum grasping size is 417 mm,and the payload on the human body is more than 90 kg,which has exceeded most existing soft ***,the grasping force prediction of the gripper is achieved through theoretical *** primary contribution of this work is to overcome the size and payload limits of current soft grippers and implement a human-grasping experiment based on the soft-grasping method.
Nowadays, artificial intelligence-based tasks are imposing increasing demands on computation resources and energy consumption. Unmanned aerial vehicles (UAVs) are widely utilized in mobile edge computing (MEC) due to ...
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The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on leveraging the inherent language generat...
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With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today's complex social networks. For the past few years, the...
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The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning ***,the communication overhead is a major bottleneck that hampers the scalabili...
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The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning ***,the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning *** this paper,we design two communication-efficient algorithms for distributed learning *** first one is named EF-SIGNGD,in which we use the 1-bit(sign-based) gradient quantization method to save the communication ***,the error feedback technique,i.e.,incorporating the error made by the compression operator into the next step,is employed for the convergence *** second algorithm is called LE-SIGNGD,in which we introduce a well-designed lazy gradient aggregation rule to EF-SIGNGD that can detect the gradients with small changes and reuse the outdated ***-SIGNGD saves communication costs both in transmitted bits and communication ***,we show that LE-SIGNGD is convergent under some mild *** effectiveness of the two proposed algorithms is demonstrated through experiments on both real and synthetic data.
End-to-end training has emerged as a prominent trend in speech recognition, with Conformer models effectively integrating Transformer and CNN architectures. However, their complexity and high computational cost pose d...
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