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Found 9 papers, 4 papers with code
Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating
Post-market surveillance, the practice of monitoring the safe use of pharmaceutical drugs is an important part of pharmacovigilance.
Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images
In this work, we propose a novel hip landmark detection model by integrating the Topological GCN (TGCN) with an Improved Conformer (TGCN-ICF) into a unified frame-work to improve detection performance.
Cross-to-merge training with class balance strategy for learning with noisy labels
Although sample selection is a mainstream method in the field of learning with noisy labels, which aims to mitigate the impact of noisy labels during model training, the testing performance of these methods exhibits significant fluctuations across different noise rates and types.
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Learning with noisy labels
on Clothing1M
Distillation Improves Visual Place Recognition for Low Quality Images
Real-time visual localization often utilizes online computing, for which query images or videos are transmitted to remote servers for visual place recognition (VPR).
pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting
However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on.
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Time series forecasting is widely used in business intelligence, e. g., forecast stock market price, sales, and help the analysis of data trend.
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications.
FANDA: A Novel Approach to Perform Follow-up Query Analysis
NLIDB allow users to search databases using natural language instead of SQL-like query languages.
Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications.
