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Found 25 papers, 6 papers with code
Towards Explainable Automated Neuroanatomy
It is based on the automated recognition of interpretable features obtained by analyzing the shapes of cells.
Effective resistance in metric spaces
One attractive application of ER is to point clouds, i. e. graphs whose vertices correspond to IID samples from a distribution over a metric space.
Active learning using region-based sampling
We present a general-purpose active learning scheme for data in metric spaces.
Structure from Voltage
Effective resistance (ER) is an attractive way to interrogate the structure of graphs.
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
There has been a surge of works bridging MCMC sampling and optimization, with a specific focus on translating non-asymptotic convergence guarantees for optimization problems into the analysis of Langevin algorithms in MCMC sampling.
StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm
With a novel sub-sampling scheme, StreaMRAK reduces memory and computational complexities by creating a sketch of the original data, where the sub-sampling density is adapted to the bandwidth of the kernel and the local dimensionality of the data.
Optimal Online Learning using Potential Functions
We study a family of potential functions for online learning.
Audio scene monitoring using redundant ad-hoc microphone array networks
The DOAs are fed to a fusion center, concatenated, and used to perform the localization based on two proposed methods, which require only few labeled source locations (anchor points) for training.
Experimental Design for Bathymetry Editing
We describe an application of machine learning to a real-world computer assisted labeling task.
An adaptive nearest neighbor rule for classification
We introduce a variant of the $k$-nearest neighbor classifier in which $k$ is chosen adaptively for each query, rather than supplied as a parameter.
Faster Boosting with Smaller Memory
State-of-the-art implementations of boosting, such as XGBoost and LightGBM, can process large training sets extremely fast.
Tell Me Something New: A New Framework for Asynchronous Parallel Learning
We present a novel approach for parallel computation in the context of machine learning that we call "Tell Me Something New" (TMSN).
Robust Landmark Detection for Alignment of Mouse Brain Section Images
In this work we describe the first steps in developing a semi-automated system to construct a histology at- las of mouse brainstem that combines atlas-guided annotation, landmark-based registration and atlas generation in an iterative framework.
The Active Atlas: Combining 3D Anatomical Models with Texture Detectors
We have developed a digital atlas methodology that combines information about the 3D organization of the brain and the detailed texture of neurons in different structures.
Typical Stability
We show that typical stability can control generalization error in adaptive data analysis even when the samples in the dataset are not necessarily independent and when queries to be computed are not necessarily of bounded-sensitivity as long as the results of the queries over the dataset (i. e., the computed statistics) follow a distribution with a "light" tail.
Optimal Binary Classifier Aggregation for General Losses
We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data.
Scalable Semi-Supervised Aggregation of Classifiers
We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers.
Optimally Combining Classifiers Using Unlabeled Data
We develop a worst-case analysis of aggregation of classifier ensembles for binary classification.
The Fast Convergence of Incremental PCA
We consider a situation in which we see samples in $\mathbb{R}^d$ drawn i. i. d.
PAC-Bayes with Minimax for Confidence-Rated Transduction
We consider using an ensemble of binary classifiers for transductive prediction, when unlabeled test data are known in advance.
Non-Convex Boosting Overcomes Random Label Noise
The sensitivity of Adaboost to random label noise is a well-studied problem.
Co-adaptation in a Handwriting Recognition System
We found that both machine adaptation and human adaptation have significant impact on the input rate and must be considered together in order to improve the efficiency of the system as a whole.
A Parameter-free Hedging Algorithm
Previous algorithms for learning in this framework have a tunable learning rate parameter, and a major barrier to using online-learning in practical applications is that it is not understood how to set this parameter optimally, particularly when the number of actions is large.
Learning the structure of manifolds using random projections
We present a simple variant of the k-d tree which automatically adapts to intrinsic low dimensional structure in data.
