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Found 15 papers, 13 papers with code
From Haystack to Needle: Label Space Reduction for Zero-shot Classification
We present Label Space Reduction (LSR), a novel method for improving zero-shot classification performance of Large Language Models (LLMs).
landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images
Although many general-purpose tools exist for landmark localization in classical computer vision tasks, such as pose estimation, they lack the specialized features and modularity necessary for anatomical landmark localization applications in the medical domain.
Conformal Prediction for Dose-Response Models with Continuous Treatments
Understanding the dose-response relation between a continuous treatment and the outcome for an individual can greatly drive decision-making, particularly in areas like personalized drug dosing and personalized healthcare interventions.
Conformal Predictive Systems Under Covariate Shift
Conformal Predictive Systems (CPS) offer a versatile framework for constructing predictive distributions, allowing for calibrated inference and informative decision-making.
Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects
The approaches leverage weighted conformal predictive systems (WCPS), Monte Carlo sampling, and CATE meta-learners to generate predictive distributions of individual treatment effect (ITE) that could enhance individualized decision-making.
tsdownsample: high-performance time series downsampling for scalable visualization
We achieve this optimization by leveraging low-level SIMD instructions and multithreading capabilities in Rust.
Perfectly predicting ICU length of stay: too good to be true
A paper of Alsinglawi et al was recently accepted and published in Scientific Reports.
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models.
Ranked #1 on
Multimodal Sleep Stage Detection
on Sleep-EDF-SC
Automatic Sleep Stage Classification
BIG-bench Machine Learning
+3
Plotly-Resampler: Effective Visual Analytics for Large Time Series
We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization.
Powershap: A Power-full Shapley Feature Selection Method
Benchmarks and simulations show that powershap outperforms other filter methods with predictive performances on par with wrapper methods while being significantly faster, often even reaching half or a third of the execution time.
R-GCN: The R Could Stand for Random
The inception of the Relational Graph Convolutional Network (R-GCN) marked a milestone in the Semantic Web domain as a widely cited method that generalises end-to-end hierarchical representation learning to Knowledge Graphs (KGs).
Ranked #1 on
Node Classification
on DMGFULL
tsflex: flexible time series processing & feature extraction
$\texttt{tsflex}$ is flexible as it supports (1) multivariate time series, (2) multiple window-stride configurations, and (3) integrates with processing and feature functions from other packages, while (4) making no assumptions about the data sampling regularity, series alignment, and data type.
Overly Optimistic Prediction Results on Imbalanced Data: a Case Study of Flaws and Benefits when Applying Over-sampling
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth.
Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning
Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment.
GENESIM: genetic extraction of a single, interpretable model
The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques.
