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Found 21 papers, 12 papers with code
Retinal Vessel Segmentation via Neuron Programming
The accurate segmentation of retinal blood vessels plays a crucial role in the early diagnosis and treatment of various ophthalmic diseases.
Don't Fear Peculiar Activation Functions: EUAF and Beyond
In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF).
No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks
First, symbolic regression is used to identify optimal formulas that fit input data by utilizing base functions such as logarithmic, trigonometric, and exponential functions.
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data.
VDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variation
However, we empirically find that VDIP struggles with processing image details and tends to generate suboptimal results when the blur kernel is large.
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
To the best of our knowledge, this is the first instance of deploying a CNN-based bearing fault diagnosis model on an FPGA.
Spatial-Frequency Discriminability for Revealing Adversarial Perturbations
The vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community.
Deep ReLU Networks Have Surprisingly Simple Polytopes
Also, by characterizing the shape of polytopes, the number of faces can be a novel leverage for other problems, \textit{e. g.}, serving as a generic tool to explain the power of popular shortcut networks such as ResNet and analyzing the impact of different regularization strategies on a network's space partition.
Cloud-RAIN: Point Cloud Analysis with Reflectional Invariance
We prove a theorem to explain why Cloud-RAIN can enjoy reflection symmetry.
On Expressivity of Height in Neural Networks
In this work, beyond width and depth, we augment a neural network with a new dimension called height by intra-linking neurons in the same layer to create an intra-layer hierarchy, which gives rise to the notion of height.
Quadratic Graph Attention Network (Q-GAT) for Robust Construction of Gene Regulatory Networks
The former informs that nonlinear aggregation of quadratic neurons can amplify useful signals and suppress unwanted noise, thereby facilitating robustness, while the latter reveals that Q-GAT can leverage more features in prediction thanks to the dual attention mechanism, which endows Q-GAT with the ability to confront adversarial perturbation.
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks
In this paper, with theoretical and empirical studies, we show that quadratic networks enjoy parametric efficiency, thereby confirming that the superior performance of quadratic networks is due to the intrinsic expressive capability.
Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks
In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors.
ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities.
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework
In this paper, using a sequential Retinex decomposition strategy, we design a plug-and-play framework based on the Retinex theory for simultaneously image enhancement and noise removal.
Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis
Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits.
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
Encouraged by this inspiring theoretical result on heterogeneous networks, we directly integrate conventional and quadratic neurons in an autoencoder to make a new type of heterogeneous autoencoders.
Manifoldron: Direct Space Partition via Manifold Discovery
A neural network with the widely-used ReLU activation has been shown to partition the sample space into many convex polytopes for prediction.
On Expressivity and Trainability of Quadratic Networks
Despite promising results so far achieved by networks of quadratic neurons, there are important issues not well addressed.
Neural Network Gaussian Processes by Increasing Depth
Inspired by a width-depth symmetry consideration, we use a shortcut network to show that increasing the depth of a neural network can also give rise to a Gaussian process, which is a valuable addition to the existing theory and contributes to revealing the true picture of deep learning.
Quasi-Equivalence of Width and Depth of Neural Networks
While classic studies proved that wide networks allow universal approximation, recent research and successes of deep learning demonstrate the power of deep networks.
