AIMC Topic: Neural Networks, Computer

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Privacy Preserving Defense For Black Box Classifiers Against On-Line Adversarial Attacks.

IEEE transactions on pattern analysis and machine intelligence
Deep learning models have been shown to be vulnerable to adversarial attacks. Adversarial attacks are imperceptible perturbations added to an image such that the deep learning model misclassifies the image with a high confidence. Existing adversarial...

MODENN: A Shallow Broad Neural Network Model Based on Multi-Order Descartes Expansion.

IEEE transactions on pattern analysis and machine intelligence
Deep neural networks have achieved great success in almost every field of artificial intelligence. However, several weaknesses keep bothering researchers due to its hierarchical structure, particularly when large-scale parallelism, faster learning, b...

Ada-LISTA: Learned Solvers Adaptive to Varying Models.

IEEE transactions on pattern analysis and machine intelligence
Neural networks that are based on the unfolding of iterative solvers as LISTA (Learned Iterative Soft Shrinkage), are widely used due to their accelerated performance. These networks, trained with a fixed dictionary, are inapplicable in varying model...

HandVoxNet++: 3D Hand Shape and Pose Estimation Using Voxel-Based Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. Existing methods addressing it directly regress hand meshes via 2D convolutional neural networks, which leads to artifa...

Learn to Predict Sets Using Feed-Forward Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as image tag...

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology.

IEEE transactions on pattern analysis and machine intelligence
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the di...

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.

Journal of chemical information and modeling
The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and ma...

Wave-like patterns in parameter space interpreted as evidence for macroscopic effects resulting from quantum or quantum-like processes in the brain.

Scientific reports
Data from eight numerosity estimation experiments reliably exhibit wave-like patterns in plots of the standard deviations of the response times along the abstract parameter of the magnitude of the error in the numerosity estimation. An explanation fo...

MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition.

PloS one
In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of...

The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data.

Clinical EEG and neuroscience
Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clini...