Machine Learning
Machine learning frameworks, algorithms, and training systems
Repositories
(7)huggingface/transformers
microsoft/onnxruntime
mlflow/mlflow
pytorch/pytorch
ray-project/ray
scikit-learn/scikit-learn
tensorflow/tensorflow
Papers
(373)Calibration of Machine Learning Models
The evaluation of machine learning models is a crucial step before their application because it is essential to assess how well a model will behave for every single case. In many real applications, not only is it important to know the âtotalâ or the ...
Efficient additive kernels via explicit feature maps
Maji and Berg have recently introduced an explicit feature map approximating the intersection kernel. This enables efficient learning methods for linear kernels to be applied to the non-linear intersection kernel, expanding the applicability of this ...
Feature hashing for large scale multitask learning.
Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random s...
Generalized RBF feature maps for Efficient Detection
Kernel methods yield state-of-the-art performance in certain applications such as image classification and object detection. However, large scale problems require machine learning techniques of at most linear complexity and these are usually limited ...
Learning to Find Pre-Images.
Travel arrangements and flight ticket booking via internet is widely used nowadays and follow already certain standards. Although increasing activity for multimedia/web education components can be observed, we are far away from standards in this impo...
Multidimensional Binary Search Trees Used for Associative Searching.
This paper develops the multidimensional binary search tree (or k -d tree, where k is the dimensionality of the search space) as a data structure for storage of information to be retrieved by associative searches. The k -d tree is defined and example...
On classification, ranking, and probability estimation.
The aim of this thesis was the study of respiration in ocean margin \nsediments and the assessments of tools needed for this purpose.\n \n \nThe first study was on the biological pump and global respiration \npatterns in the deep ocean using an empir...
On Linear DETs.
This paper investigates the properties of a popular ROC variant - the detection error trade-off plot (DET). In particular, we derive a set of conditions on the underlying probability distributions to produce linear DET plots in a generalized setting....
Random Features for Large-Scale Kernel Machines
In this paper, we contributed a stereo face recognition formulation which combines appearance and disparity/depth at feature level. We showed that the present-day passive stereovision in combination with 2D appearance images can match up to other met...
Stochastic variational inference.
AbstractTools for estimating population structure from genetic data are now used in a wide variety of applications in population genetics. However, inferring population structure in large modern data sets imposes severe computational challenges. Here...
Stop Word Lists in Free Open-source Software Packages
Open-source software packages for language processing often include stop word lists. Users may apply them without awareness of their surprising omissions (e.g. âhasnâtâ but not âhadnâtâ) and inclusions (âcomputerâ), or their incompatibility with a pa...
The DET curve in assessment of detection task performance.
Abstract : We introduce the DET Curve as a means of representing performance on detection tasks that involve a tradeoff of error types. We discuss why we prefer it to the traditional ROC Curve and offer several examples of its use in speaker recognit...