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Found 19 papers, 3 papers with code
Precise Asymptotic Analysis of Deep Random Feature Models
We provide exact asymptotic expressions for the performance of regression by an $L-$layer deep random feature (RF) model, where the input is mapped through multiple random embedding and non-linear activation functions.
FsaNet: Frequency Self-attention for Semantic Segmentation
By ablation study, we show that low frequency self-attention can achieve very close or better performance relative to full frequency even without retraining the network.
Fast OT for Latent Domain Adaptation
This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.
Sharing pattern submodels for prediction with missing values
Conversely, fitting a single shared model to the full data set relies on imputation which often leads to biased results when missingness depends on unobserved factors.
Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves
We compute precise asymptotic expressions for the learning curves of least squares random feature (RF) models with either a separable strongly convex regularization or the $\ell_1$ regularization.
Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion
Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data.
Analysis of Knowledge Transfer in Kernel Regime
Knowledge transfer is shown to be a very successful technique for training neural classifiers: together with the ground truth data, it uses the "privileged information" (PI) obtained by a "teacher" network to train a "student" network.
Community Detection and Improved Detectability in Multiplex Networks
We further demonstrate by numerical experiments that in the presence of consistent communities between different layers, consistent communities are matched, and the detectability is improved over a single layer.
Deep Adversarial Belief Networks
We present a novel adversarial framework for training deep belief networks (DBNs), which includes replacing the generator network in the methodology of generative adversarial networks (GANs) with a DBN and developing a highly parallelizable numerical algorithm for training the resulting architecture in a stochastic manner.
Sparse Generative Adversarial Network
To that end we start by dividing an image into multiple patches and modifying the role of the generative network from producing an entire image, at once, to creating a sparse representation vector for each image patch.
Joint Concept Matching based Learning for Zero-Shot Recognition
Zero-shot learning (ZSL) which aims to recognize unseen object classes by only training on seen object classes, has increasingly been of great interest in Machine Learning, and has registered with some successes.
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework
We develop a novel theoretical framework for understating OT schemes respecting a class structure.
Analysis Dictionary Learning: An Efficient and Discriminative Solution
Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems.
Analysis Dictionary Learning based Classification: Structure for Robustness
A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification.
Structured Analysis Dictionary Learning for Image Classification
We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning.
Demystifying Deep Learning: A Geometric Approach to Iterative Projections
Parametric approaches to Learning, such as deep learning (DL), are highly popular in nonlinear regression, in spite of their extremely difficult training with their increasing complexity (e. g. number of layers in DL).
Deep Dictionary Learning: A PARametric NETwork Approach
Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics.
A Universal Analysis of Large-Scale Regularized Least Squares Solutions
Precise expressions for the asymptotic performance of LASSO have been obtained for a number of different cases, in particular when the elements of the dictionary matrix are sampled independently from a Gaussian distribution.
Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery
Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering are beset by local minima, which are sometimes drastically suboptimal.
