This work shows that the demonstration of Proposition 15 of Germain et al. (2015) is awed and the proposition is false in a general setting. This proposition gave an inequality that upper-bounds the variance of the ma...
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This work shows that the demonstration of Proposition 15 of Germain et al. (2015) is awed and the proposition is false in a general setting. This proposition gave an inequality that upper-bounds the variance of the margin of a weighted majority vote classifier. Even though this aw has little impact on the validity of the other results presented in Germain et al. (2015), correcting it leads to a deeper understanding of the C-bound, which is a key inequality that upper-bounds the risk of a majority vote classifier by the moments of its margin, and to a new result, namely a lower-bound on the C-bound. Notably, Germain et al.'s statement that "the C-bound can be arbitrarily small" is invalid in presence of irreducible error in learning problems with label noise. In this erratum, we pinpoint the mistake present in the demonstration of the said proposition, we give a corrected version of the proposition, and we propose a new theoretical lower bound on the C-bound.
The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gai...
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The performance of the state-of-the-art Deep Reinforcement algorithms such as Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic for generating a quadruped walking gait in a virtual environment was presented in previous research work titled “A Comparison of PPO, TD3, and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation”. We demonstrated that the Soft Actor-Critic Reinforcement algorithm had the best performance generating the walking gait for a quadruped in certain instances of sensor configurations in the virtual environment. In this work, we present the performance analysis of the state-of-the-art Deep Reinforcement algorithms above for quadruped walking gait generation in a physical environment. The performance is determined in the physical environment by transfer learning augmented by real-time reinforcement learning for gait generation on a physical quadruped. The performance is analyzed on a quadruped equipped with a range of sensors such as position tracking using a stereo camera, contact sensing of each of the robot legs through force resistive sensors, and proprioceptive information of the robot body and legs using nine inertial measurement units. The performance comparison is presented using the metrics associated with the walking gait: average forward velocity (m/s), average forward velocity variance, average lateral velocity (m/s), average lateral velocity variance, and quaternion root mean square deviation. The strengths and weaknesses of each algorithm for the given task on the physical quadruped are discussed.
Hand gesture recognition is one of the interesting problems of computer Vision. It has a wide range of applications in the fields of Human-computer Interaction, Robotics, Sign language interpretation, Augmented Realit...
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This study proposes an energy- and area-efficient CMOS neuron and analog max pooling circuit for RRAM-based convolutional neural network (CNN) accelerators. The proposed max pooling circuit implements a 2 x 2 max pool...
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In the realm of secure training protocols for machine learning models within adversarial networks, the proposed methodology encompasses three key algorithms: Federated Learning with Differential Privacy (FedDP), Adver...
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In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretrained domain-specific models. To do so...
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This paper presents a method that uses convolutional neural networks for audio recognition in an open-set scenario. The audio sounds in an open-set scenario are usually out of the training data distribution, which nec...
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Pharmaceutical Supply Chains (PSCs) are a combination of processes and networks involved in the production, distribution, and delivery of pharmaceutical products from the stage of raw materials till they reach the end...
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In the rapidly evolving domains of AI and Internet tech, face recognition, a key machine learning application, is increasingly used in security, identity verification, and public monitoring. As this technology progres...
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Failure is a systemic error that affects overall system performance and may eventually crash across the entire *** Real-Time Systems(RTS),deadline is the key to successful completion of the *** tasks effectively meet ...
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Failure is a systemic error that affects overall system performance and may eventually crash across the entire *** Real-Time Systems(RTS),deadline is the key to successful completion of the *** tasks effectively meet the deadline,it means the system is working in pristine ***,missing the deadline means a systemic fault due to which the system can crash(hard RTS)or degrade inclusive performance(soft RTS).To fine-tune the RTS,tolerance is the critical issue and must be handled with extreme *** article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in *** backup method has been derived to prevent the system from being recursively migrating the same *** any task migrates three times,this migrated task will get shifted to the backup *** backup queue assigns the task to a backup processor and is destined for final *** performance evaluation purposes,a relative graph between fault and failure rates,failure and total processor utilization along with other averages have been ***,these archived results are compared with fault-tolerant Earliest Deadline First(EDF)and Rate Monotonic Scheduling(RMS)algorithms independently in relatively similar *** comparisons show better performance against overloading conditions.
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