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Found 12 papers, 5 papers with code
Learning Tree-Structured Composition of Data Augmentation
Existing augmentation methods such as RandAugment randomly sample from a list of pre-selected transformations, while methods such as AutoAugment apply advanced search to optimize over an augmentation set of size $k^d$, which is the number of transformation sequences of length $d$, given a list of $k$ transformations.
CAFA-evaluator: A Python Tool for Benchmarking Ontological Classification Methods
We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies.
Current and future directions in network biology
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales.
Leveraging Structure for Improved Classification of Grouped Biased Data
To model the inherent structure in such data, we assume the partition-projected class-conditional invariance across groups, defined in terms of the group-agnostic feature space.
Explaining Deep Tractable Probabilistic Models: The sum-product network case
We consider the problem of explaining a class of tractable deep probabilistic models, the Sum-Product Networks (SPNs) and present an algorithm ExSPN to generate explanations.
Classification in biological networks with hypergraphlet kernels
Graphs, however, often suffer from information loss when modeling physical systems due to their inability to accurately represent multiobject relationships.
Recovering True Classifier Performance in Positive-Unlabeled Learning
A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data.
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
However, sophisticated learning models are computationally unfeasible for data with millions of features.
Estimating the class prior and posterior from noisy positives and unlabeled data
We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data.
A new class of metrics for learning on real-valued and structured data
We propose a new class of metrics on sets, vectors, and functions that can be used in various stages of data mining, including exploratory data analysis, learning, and result interpretation.
Nonparametric semi-supervised learning of class proportions
This problem can be decomposed into two steps: (i) the development of accurate predictors that discriminate between positive and unlabeled data, and (ii) the accurate estimation of the prior probabilities of positive and negative examples.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
