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Found 9 papers, 1 papers with code
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing
Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction.
Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings
In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning.
Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks
However, when this method uses backpropagation to obtain gradients, it will cause noise in the saliency map, and even locate features that are irrelevant to decisions.
The JDDC 2.0 Corpus: A Large-Scale Multimodal Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service
We present the solutions of top-5 teams participating in the JDDC multimodal dialogue challenge based on this dataset, which provides valuable insights for further researches on the multimodal dialogue task.
Anomalous Motion of Particle Levitated by Laguerre-Gaussian beam
We calculated the scattering force distribution of a micro-particle trapped in an optical tweezers formed by the strongly focused LG beam, and showed that there exist stable trajectories of the particle that controlled by the negative torque.
Optics
Robust Federated Learning with Noisy Communication
Nevertheless, it is impractical to achieve a perfect acquisition of the local models in wireless communication due to noise, which also brings serious effects on federated learning.
Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning
To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types.
Recognition of Emotions using Kinects
By using the NaiveBayes, RandomForests, libSVM and SMO classification, the recognition rate of natural and unnatural emotions can reach above 70%. It is concluded that using the Kinect system can be a new method in recognition of emotions.
