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Found 8 papers, 1 papers with code
Dependability Evaluation of Stable Diffusion with Soft Errors on the Model Parameters
In this paper, the dependability of Stable Diffusion is studied focusing on soft errors in the memory that stores the model parameters; specifically, errors are injected into some critical layers of the Transformer in different blocks of the image information creator, to evaluate their impact on model performance.
Concurrent Linguistic Error Detection (CLED) for Large Language Models
Since the proposed error detection mechanism only relies on the outputs of the model, then it can be used on LLMs in which there is no access to the internal nodes.
Automatic laminectomy cutting plane planning based on artificial intelligence in robot assisted laminectomy surgery
Results: In the first stage, the average localization error of the SPU-Net algorithm for the seven key points was 0. 65mm.
Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems
In this paper, we introduce Concurrent Classifier Error Detection (CCED), a scheme to implement CED in ML systems using a concurrent ML classifier to detect errors.
Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects
Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants.
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining
Specifically, we transform text data into synthesized text images to unify the data modalities of vision and language, and enhance the language modeling capability of the sequence decoder using a proposed masked image-language modeling scheme.
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency.
Ranked #1 on
Scene Text Detection
on ICDAR 2015
(Accuracy metric)
Neural Machine Reading Comprehension: Methods and Trends
Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years.
