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Found 18 papers, 6 papers with code
DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation
Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains.
Unicorn: Unified Neural Image Compression with One Number Reconstruction
The neural model serves as the unified decoder of images while the noises and indexes corresponds to explicit representations.
Self-reconfiguration Strategies for Space-distributed Spacecraft
The joint planning of the robotic arm is then accomplished through forward and reverse kinematics.
Enhancing Vectorized Map Perception with Historical Rasterized Maps
To fully exploit a historical map, we propose two novel modules to enhance BEV features and map element queries.
GAD-Generative Learning for HD Map-Free Autonomous Driving
Deep-learning-based techniques have been widely adopted for autonomous driving software stacks for mass production in recent years, focusing primarily on perception modules, with some work extending this method to prediction modules.
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
In autonomous driving, the high-definition (HD) map plays a crucial role in localization and planning.
Zero-Shot Structure-Preserving Diffusion Model for High Dynamic Range Tone Mapping
However obtaining paired HDR and high-quality LDR images is difficult posing a challenge to deep learning based tone mapping methods.
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition
In this study, we propose a novel method for skill learning in robotic manipulation called Tactile Active Inference Reinforcement Learning (Tactile-AIRL), aimed at achieving efficient training.
NPS: A Framework for Accurate Program Sampling Using Graph Neural Network
Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings.
Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows
The ability to choose an appropriate camera view among multiple cameras plays a vital role in TV shows delivery.
Contrastive and Selective Hidden Embeddings for Medical Image Segmentation
Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning.
Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective
Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc.
ShiftAddNet: A Hardware-Inspired Deep Network
Multiplication (e. g., convolution) is arguably a cornerstone of modern deep neural networks (DNNs).
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
In this paper we will use deep learning based medical image segmentation as a vehicle and demonstrate that interestingly, machine and human view the compression quality differently.
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework
To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, effective data compression is a "must-have" feature before transferring real-time produced dataset for training or classification.
MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation
Modern deep learning enabled artificial neural networks, such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN), have achieved a series of breaking records on a broad spectrum of recognition applications.
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recently.
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks
In this work, we for the first time investigate the multi-factor adversarial attack problem in practical model optimized deep learning systems by jointly considering the DNN model-reshaping (e. g. HashNet based deep compression) and the input perturbations.
