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Found 11 papers, 4 papers with code
Data-Effective Learning: A Comprehensive Medical Benchmark
This benchmark includes a dataset with millions of data samples from 31 medical centers (DataDEL), a baseline method for comparison (MedDEL), and a new evaluation metric (NormDEL) to objectively measure data-effective learning performance.
Low-latency Space-time Supersampling for Real-time Rendering
With the rise of real-time rendering and the evolution of display devices, there is a growing demand for post-processing methods that offer high-resolution content in a high frame rate.
Instruct-NeuralTalker: Editing Audio-Driven Talking Radiance Fields with Instructions
Given a short speech video, we first build an efficient talking radiance field, and then apply the latest conditional diffusion model for image editing based on the given instructions and guiding implicit representation optimization towards the editing target.
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution
To address these problems, we propose the MVSRnet, which uses geometry information to extract sharp details from all LR multi-view to support the SR of the LR input view.
Learning Robust Image-Based Rendering on Sparse Scene Geometry via Depth Completion
Specifically, GR stage takes sparse depth map and RGB as input to predict dense depth map by exploiting the correlation between two modals.
Multiple imputation using chained random forests: a preliminary study based on the empirical distribution of out-of-bag prediction errors
In this study, a novel RF-based multiple imputation method was proposed by constructing conditional distributions the empirical distribution of out-of-bag prediction errors.
Influence of parallel computing strategies of iterative imputation of missing data: a case study on missForest
Machine learning iterative imputation methods have been well accepted by researchers for imputing missing data, but they can be time-consuming when handling large datasets.
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings
In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors.
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis
Unsupervised learned representations of polysemous words generate a large of pseudo multi senses since unsupervised methods are overly sensitive to contextual variations.
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better.
