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Found 6 papers, 0 papers with code
Dynamic Backtracking in GFlowNets: Enhancing Decision Steps with Reward-Dependent Adjustment Mechanisms
Generative Flow Networks (GFlowNets) are probabilistic models predicated on Markov flows, employing specific amortization algorithms to learn stochastic policies that generate compositional substances including biomolecules, chemical materials, and more.
Learning Spiking Neural Network from Easy to Hard task
To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability.
Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution
To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has not any external stimulation.
Multi-local Collaborative AutoEncoder
In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure.
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM).
Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints
To enhance the expression ability of traditional RBMs, in this paper, we propose pairwise constraints restricted Boltzmann machine with Gaussian visible units (pcGRBM) model, in which the learning procedure is guided by pairwise constraints and the process of encoding is conducted under these guidances.
