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Found 7 papers, 0 papers with code
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery.
Deep Joint Source-Channel Coding for Wireless Image Transmission with Semantic Importance
Specifically, we first propose semantic importance weight calculation method, which is based on the gradient of intelligent task's perception results with respect to the features.
Deep Joint Source-Channel Coding Based on Semantics of Pixels
The semantic information of the image for intelligent tasks is hidden behind the pixels, and slight changes in the pixels will affect the performance of intelligent tasks.
Semantic-assisted image compression
Experimental results show that the proposed SAIC method can retain more semantic-level information and achieve better performance of downstream AI tasks compared to the traditional deep learning-based method and the advanced perceptual method at the same compression ratio.
Bandwidth and Power Allocation for Task-Oriented SemanticCommunication
The first subproblem is a compression ratio optimization problem with a given resource allocation scheme, which is solved by a enumeration algorithm.
Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction
JAL-MTP use a Social to Lane (S2L) module to jointly represent the static lane and the dynamic motion of the neighboring agents as instance-level lane, a Recurrent Lane Attention (RLA) mechanism for utilizing the instance-level lanes to predict the map-adaptive future trajectories and two selectors to identify the typical and reasonable trajectories.
Semantic Communications With AI Tasks
A radical paradigm shift of wireless networks from ``connected things'' to ``connected intelligence'' undergoes, which coincides with the Shanno and Weaver's envisions: Communications will transform from the technical level to the semantic level.
