Activation functions in deep learning: A comprehensive survey and benchmark
Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …
problems. Various types of neural networks have been introduced to deal with different types …
Review on deep learning applications in frequency analysis and control of modern power system
The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …
regional power grids in wide area and large scale have led the modern power system to …
Multimodal learning with transformers: A survey
Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles
Object detection is a significant downstream task in computer vision. For the on-board edge
computing platforms, a giant model is difficult to achieve the real-time detection requirement …
computing platforms, a giant model is difficult to achieve the real-time detection requirement …
Strongsort: Make deepsort great again
Y Du, Z Zhao, Y Song, Y Zhao, F Su… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly,
remarkable progresses have been achieved. However, the existing methods tend to use …
remarkable progresses have been achieved. However, the existing methods tend to use …
DeePMD-kit v2: A software package for deep potential models
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …
simulations using machine learning potentials known as Deep Potential (DP) models. This …
Resmlp: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image
classification. It is a simple residual network that alternates (i) a linear layer in which image …
classification. It is a simple residual network that alternates (i) a linear layer in which image …
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …
reduce the size of neural networks by selectively pruning components. Similarly to their …
Polyp-pvt: Polyp segmentation with pyramid vision transformers
Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …
when exchanging information between the encoder and decoder: 1) taking into account the …
Molecular contrastive learning of representations via graph neural networks
Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …
drug discovery. However, labelled molecule data can be expensive and time consuming to …