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Found 10 papers, 2 papers with code
Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations
Existing research often posits spurious features as "easier" to learn than core features in neural network optimization, but the impact of their relative simplicity remains under-explored.
Deep Pairwise Learning To Rank For Search Autocomplete
Autocomplete (a. k. a "Query Auto-Completion", "AC") suggests full queries based on a prefix typed by customer.
Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm
In this paper, a graph-based nonlocal total variation method (NLTV) is proposed for unsupervised classification of hyperspectral images (HSI).
A Harmonic Extension Approach for Collaborative Ranking
We present a new perspective on graph-based methods for collaborative ranking for recommender systems.
Fast Clustering and Topic Modeling Based on Rank-2 Nonnegative Matrix Factorization
In this paper, we propose a fast method for hierarchical clustering and topic modeling called HierNMF2.
Hardware Compliant Approximate Image Codes
In recent years, several feature encoding schemes for the bags-of-visual-words model have been proposed.
piCholesky: Polynomial Interpolation of Multiple Cholesky Factors for Efficient Approximate Cross-Validation
The dominant cost in solving least-square problems using Newton's method is often that of factorizing the Hessian matrix over multiple values of the regularization parameter ($\lambda$).
Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization
The effectiveness of this approach is illustrated on several synthetic and real-world hyperspectral images, and shown to outperform standard clustering techniques such as k-means, spherical k-means and standard NMF.
Symmetric Nonnegative Matrix Factorization for Graph Clustering
Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).
