Recent advances in biological research have seen the emergence of high-throughput technologies with numerous applications that allow the study of biological mechanisms at an unprecedented depth and scale.
This paper introduces TopicNet, a new Python module for topic modeling.
Finally, we show that the use of ensemble classifiers helps resolve the crucial model selection step, and that most errors in the identification of RRLs are related to low quality observations of some sources or to the difficulty to resolve the RRL-C type given the date.
For the first question, we conduct a thorough empirical study over analysis sets and find that in addition to the unstable final performance, the instability exists all along the training curve.
In this work, we develop SINDy-PI (parallel, implicit), a robust variant of the SINDy algorithm to identify implicit dynamics and rational nonlinearities.
Second, we integrate the whole evaluation codes into a code library and release this code library to the public for better development of this field.
This paper presents LIBTwinSVM, a free, efficient, and open source library for Twin Support Vector Machines (TSVMs).
A reciprocal LASSO (rLASSO) regularization employs a decreasing penalty function as opposed to conventional penalization methods that use increasing penalties on the coefficients, leading to stronger parsimony and superior model selection relative to traditional shrinkage methods.
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
We characterize CNN's architecture by different attributes, and demonstrate that the attributes can be predictive of the networks' performance in two specific computer vision-based physics problems -- event vertex finding and hadron multiplicity classification in the MINERvA experiment at Fermi National Accelerator Laboratory.