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Found 12 papers, 2 papers with code
Efficient NTK using Dimensionality Reduction
Recently, neural tangent kernel (NTK) has been used to explain the dynamics of learning parameters of neural networks, at the large width limit.
More Side Information, Better Pruning: Shared-Label Classification as a Case Study
We are given a multi-class prediction problem, combined with a (possibly pre-trained) network architecture for solving it on a given instance distribution, and also a method for pruning the network to allow trading off prediction speed with accuracy.
Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice
Motivated by these facts, we propose to replace a dense linear layer in any neural network by an architecture based on the butterfly network.
Spatial contrasting for deep unsupervised learning
Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks.
Semi-supervised deep learning by metric embedding
Deep networks are successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples.
Deep unsupervised learning through spatial contrasting
Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks.
Deep metric learning using Triplet network
Deep learning has proven itself as a successful set of models for learning useful semantic representations of data.
Reducing Dueling Bandits to Cardinal Bandits
We present algorithms for reducing the Dueling Bandits problem to the conventional (stochastic) Multi-Armed Bandits problem.
Bandit Online Optimization Over the Permutahedron
Unfortunately, CombBand requires at each step an $n$-by-$n$ matrix permanent approximation to within improved accuracy as $T$ grows, resulting in a total running time that is super linear in $T$, making it impractical for large time horizons.
Online Ranking: Discrete Choice, Spearman Correlation and Other Feedback
Given a set $V$ of $n$ objects, an online ranking system outputs at each time step a full ranking of the set, observes a feedback of some form and suffers a loss.
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity
Given a set $V$ of $n$ elements we wish to linearly order them using pairwise preference labels which may be non-transitive (due to irrationality or arbitrary noise).
Streaming k-means approximation
We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting.
