However, surveys have shown that giving more control to an AI in self-driving cars is accompanied by a degree of uneasiness by passengers.
To protect sensitive training data, differentially private stochastic gradient descent (DP-SGD) has been adopted in deep learning to provide rigorously defined privacy.
In this paper, we develop a method for grounding medical text into a physically meaningful and interpretable space corresponding to a human atlas.
In this work, we deviate from recent, popular task settings and consider the problem under an autonomous vehicle scenario.
Ranked #2 on Referring Expression Comprehension on Talk2Car
In this paper, we aim to develop a self-supervised grounding of Covid-related medical text based on the actual spatial relationships between the referred anatomical concepts.
Finally, when compared to a well-tuned fully supervised baseline that yielded a balanced accuracy (BA) of $70. 9\pm0. 8%$ on the test set, Semixup had comparable performance -- BA of $71\pm0. 8%$ $(p=0. 368)$ while requiring $6$ times less labeled data.
First, we investigate the theoretical differences in a risk minimization framework and question the existence of a weighted cross-entropy loss with weights theoretically optimized to surrogate Dice or Jaccard.
Superpixel algorithms are a common pre-processing step for computer vision algorithms such as segmentation, object tracking and localization.
In particular, by working in scattering space, we achieve competitive results both for supervised and unsupervised learning tasks, while making progress towards constructing more interpretable CNNs.
Learning this function brings two benefits: it implicitly models the desired structure or sparsity properties to form suitable priors, and it can be tailored to the specific problem of edge structure discovery, rather than maximizing data likelihood.
For some class of probability distributions, an edge between two variables is present if and only if the corresponding entry in the precision matrix is non-zero.
Such a test enables us to determine whether one source variable is significantly more dependent on a first target variable or a second.
This paper introduces FGVC-Aircraft, a new dataset containing 10, 000 images of aircraft spanning 100 aircraft models, organised in a three-level hierarchy.
The k-support norm has been recently introduced to perform correlated sparsity regularization.
Provided this mapping is based on polynomial time computable statistics of a sentence, we show that the existance of a margin between these data points implies the existance of a polynomial time solver for that SAT subset based on the Davis-Putnam-Logemann-Loveland algorithm.
A standard approach to learning object category detectors is to provide strong supervision in the form of a region of interest (ROI) specifying each instance of the object in the training images.
Resting state activity is brain activation that arises in the absence of any task, and is usually measured in awake subjects during prolonged fMRI scanning sessions where the only instruction given is to close the eyes and do nothing.
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters.