We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of...
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We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a 2D navigation task. We show how the use of animal videos improves performance over RL algorithms that do not leverage such observations.
Deep learning is a powerful subset of synthetic intelligence, and it has the potential to significantly enhance the skills of machine getting to know. it's miles primarily based on the idea of artificial neural ne...
ISBN:
(数字)9798350357769
ISBN:
(纸本)9798350357776
Deep learning is a powerful subset of synthetic intelligence, and it has the potential to significantly enhance the skills of machine getting to know. it's miles primarily based on the idea of artificial neural networks, which might be laptop fashions that are capable of gaining knowledge of from records and making decisions, just like the manner a human mind works. Deep mastering isn't the same as traditional system mastering algorithms in that it can technique complex inputs with multiple layers of abstraction, main to greater accurate evaluation. Deep studying algorithms are capable of unexpectedly examine raw facts and generate styles, making them perfect for actual-time statistics analysis. Deep studying is capable of as it should be predicting behaviors and trends, which may be used to optimize procedures and enhance operational efficiencies. Deep studying models can also be used for automated fault detection and predictive renovation, permitting businesses to count on and prevent capacity troubles. Additionally, deep mastering models frequently offer quicker consequences than other device studying or conventional techniques, as well as permitting an extra diploma of control over the resolution of the data being analyzed. This has widespread implications for real-time programs, such as medical diagnostics, independent automobiles, and manufacturing monitoring.
This paper presents a new transistor placement method applied to the ASTRAN EDA tool, an open-source solution for the automatic design of complex digital gates. Although it currently reaches an optimized solution thro...
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Information overload has been one of the challenges regarding information on the Internet. It is no longer a matter of information access, instead, the focus has shifted towards the quality of the retrieved data. Part...
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This Research to Practice Full Paper seeks to map the assets of ACM / IEEE, SBC and SWEBOK guide specifically in the knowledge area of Software Process Improvement (SPI) to create a set of contents and competences to ...
This Research to Practice Full Paper seeks to map the assets of ACM / IEEE, SBC and SWEBOK guide specifically in the knowledge area of Software Process Improvement (SPI) to create a set of contents and competences to be addressed in the classroom, aligned with international and Brazilian references using the good practices of CMMI DEV v2.0 and MR-MPS-SW 2023. The teacher is the main gear for conducting teaching, research, and extension, being responsible for selecting the methods to be used to improve the teaching-learning process. SPI is a set of partially ordered steps with the intention of achieving a goal within the context of software development and involves understanding existing processes and changing them to increase product quality and / or reduce costs and development time. Thus, teaching SPI needs to be addressed in computing courses to meet the wishes of the software industry.
Deep learning is a powerful subset of synthetic intelligence, and it has the potential to significantly enhance the skills of machine getting to know. it's miles primarily based on the idea of artificial neural ne...
Deep learning is a powerful subset of synthetic intelligence, and it has the potential to significantly enhance the skills of machine getting to know. it's miles primarily based on the idea of artificial neural networks, which might be laptop fashions that are capable of gaining knowledge of from records and making decisions, just like the manner a human mind works. Deep mastering isn't the same as traditional system mastering algorithms in that it can technique complex inputs with multiple layers of abstraction, main to greater accurate evaluation. Deep studying algorithms are capable of unexpectedly examine raw facts and generate styles, making them perfect for actual-time statistics analysis. Deep studying is capable of as it should be predicting behaviors and trends, which may be used to optimize procedures and enhance operational efficiencies. Deep studying models can also be used for automated fault detection and predictive renovation, permitting businesses to count on and prevent capacity troubles. Additionally, deep mastering models frequently offer quicker consequences than other device studying or conventional techniques, as well as permitting an extra diploma of control over the resolution of the data being analyzed. This has widespread implications for real-time programs, such as medical diagnostics, independent automobiles, and manufacturing monitoring.
A novel method has been developed to evaluate the water diffusion dynamics in gelatin-based gels, based on transient terahertz time-domain spectroscopy measurements. Such gels are widely used models for human skin. &#...
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Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery dete...
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ISBN:
(数字)9798350350067
ISBN:
(纸本)9798350350074
Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery detection techniques like Copy-Move Forgery Detection (CMFD) cannot detect these forged documents because they are unable to identify a sufficient number of effective keypoints in homogeneous areas, leading to inaccurate and inefficient results. SURF stands for Speeded-Up Robust Features and is used in this paper along with A-KAZE and Scale-Invariant Feature Transforms. According to our experiment, A-KAZE offers superior detection performance under diverse attacks, especially when it comes to large-scale attacks targeting homogeneous regions. A-KAZE is found to be more accurate than SIFT, SURF, and A-KAZE when applied to the NB-CASIA dataset, achieving detection accuracies of $\mathbf{8 9. 2 \%}, \mathbf{9 3. 9 \%}$ and $\mathbf{9 8. 9 8 \%}$.)
Breast cancer is a malignant tumor with a high mortality rate among women. Therefore, it is necessary to develop novel therapies to effectively treat this disease. In this study, iron selenide nanorods (FeSe2 NRs) wer...
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In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf 0.5 Zr 0.5 O 2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MN...
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ISBN:
(数字)9798350386073
ISBN:
(纸本)9798350386080
In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf
0.5
Zr
0.5
O
2
(HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST handwritten digit dataset is used for training and inference processes in multilayer perceptron (MLP) neural networks. With the highly optimized synaptic device, the results demonstrate high training and inference accuracy, achieving up to 97% for the MLP, approaching the accuracy of software implementations.
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