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Found 5 papers, 3 papers with code
Decompose Semantic Shifts for Composed Image Retrieval
Composed image retrieval is a type of image retrieval task where the user provides a reference image as a starting point and specifies a text on how to shift from the starting point to the desired target image.
Online Portfolio Management via Deep Reinforcement Learning with High-Frequency Data
In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation.
SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs
Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism, but currently, the performance of SNNs is still behind Artificial Neural Networks (ANNs).
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks
This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness.
High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning
Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding.
