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Found 11 papers, 1 papers with code
Policy Optimization with Smooth Guidance Learned from State-Only Demonstrations
The sparsity of reward feedback remains a challenging problem in online deep reinforcement learning (DRL).
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
Deep reinforcement learning (DRL) faces significant challenges in addressing the hard-exploration problems in tasks with sparse or deceptive rewards and large state spaces.
An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics
This approach incorporates the physics-based heat kernel and DropNode technique to transform each static graph into a sequence of temporal ones.
Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes
Specifically, low-frequency eigenmodes, which are considered sufficient to capture the essence of the functional network, contribute little to functional connectivity reconstruction in transmodal regions, resulting in structure-function decoupling along the unimodal-transmodal gradient.
Parameter Convex Neural Networks
Deep learning utilizing deep neural networks (DNNs) has achieved a lot of success recently in many important areas such as computer vision, natural language processing, and recommendation systems.
A novel feed rate scheduling method based on Sigmoid function with chord error and kinematics constraints
With the consideration of kinematic limitation and machining efficiency, a time-optimal feed rate adjustment algorithm is proposed to further adjust feed rate value at breaking points.
Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training
First-order methods like stochastic gradient descent(SGD) are recently the popular optimization method to train deep neural networks (DNNs), but second-order methods are scarcely used because of the overpriced computing cost in getting the high-order information.
A novel S-shape based NURBS interpolation with acc-jerk- Continuity and round-off error elimination
The algorithm consists of three modules: bidirectional scanning module, velocity scheduling module and round-off error elimination module.
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network
Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning.
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs
The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction , etc.
Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes.
