Key: Robotic Manipulation, Equivariance, Graph Neural Networks, Reinforcement Learning, Deformable Objects ExpEnv: Rigid insertion, rope manipulation, cloth manipulation with multiple end-effectors M^3PC: Test-time Model Predictive Control using Pretrained Masked Trajectory Model Kehan Wen, Yutong Hu, Yao...
Layer normalization also provides better generalization and performance, particularly in deep networks. Drawbacks: Layer normalization introduces additional computational overhead, as it requires calculating the mean and variance of each layer's inputs. This overhead can make training slower compared to ...
The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods o
Artificial neural networks (ANNs) have gained considerable momentum in the past decade. Although at first the main task of the ANN paradigm was to tune the connection weights in fixed-architecture networks, there has recently been growing interest in evolving network architectures toward the goal of...
Inputoutput channel for a high-speed computing sy 优质文献 相似文献 参考文献 引证文献A gate-delay model for high-speed CMOS circuits As signal speeds increase and gate delays decrease for high-performance digital integrated circuits, the gate delay modeling problem becomes increasingly m... F Dart...
Ac, Bc and Cc represent the system, input and output matrices of appropriate dimensions. We illustrate our work using the motivating case study of a vision-based lateral control system model, commonly referred to as a lane keeping assist system (LKAS). Our camera sensor captures the image ...
At the core of a machine learning algorithm is a learning model that describes the relationship between input data and output rules. Typical learning models include artificial neural networks (ANNs), genetic algorithms, and regression analysis, to name just a few. This paper draws attention to ANN...
adaptive temperature scaling for robust calibration of deep neural networks [Paper] rethinking degradation: radiograph super-resolution via aid-srgan [Paper] an intertwined neural network model for eeg classification in brain-computer interfaces [Paper] machine learning-based eeg applications and ...
FeaVis is an algorithm designed only to visualize the features of convolutional neural networks (CNNs). However, given a large-scale cross-modal foundation model like our BriVL, we can visualize any text input by using the joint image-text embedding space as the bridge. Concretely, we first...
The present study used a convolutional neural network (CNN) because the image data of the elemental distributions of tribofilms were used as the input values. The CNN, which is a kind of neural network, is used to predict the output value from image data. This is because neural network mo...