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Found 20 papers, 4 papers with code
Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
Model-based deep learning methods such as \emph{loop unrolling} (LU) and \emph{deep equilibrium model} (DEQ) extensions offer outstanding performance in solving inverse problems (IP).
Slepian Beamforming: Broadband Beamforming using Streaming Least Squares
Alongside a careful discussion of this model and how to choose its parameters we show how to fit the model to new blocks of samples as they are received, producing a streaming output.
Learned Proximal Operator for Solving Seismic Deconvolution Problem
Due to the sparse nature of the reflectivity sequence, spike-promoting regularizers such as the $\ell_1$-norm are frequently used.
New Equivalences Between Interpolation and SVMs: Kernels and Structured Features
The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick.
Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solver
In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements.
Learning Sinkhorn divergences for supervised change point detection
We present a novel change point detection framework that uses true change point instances as supervision for learning a ground metric such that Sinkhorn divergences can be then used in two-sample tests on sliding windows to detect change points in an online manner.
Active metric learning and classification using similarity queries
Motivated by this, we propose a novel unified query framework that can be applied to any problem in which a key component is learning a representation of the data that reflects similarity.
Harmless interpolation in regression and classification with structured features
Our results recover prior independent-features results (with a much simpler analysis), but they furthermore show that harmless interpolation can occur in more general settings such as features that are a bounded orthonormal system.
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data.
Thomson's Multitaper Method Revisited
This is useful in problems where many samples are taken, and thus, using many tapers is desirable.
Semi-supervised sequence classification through change point detection
We show that change points provide examples of similar/dissimilar pairs of sequences which, when coupled with labeled, can be used in a semi-supervised classification setting.
Simultaneous Preference and Metric Learning from Paired Comparisons
The underlying assumption in this model is that a smaller distance between $\mathbf{u}$ and an item $\mathbf{x_j}$ indicates a stronger preference for $\mathbf{x_j}$.
Dynamic Knowledge embedding and tracing
In this paper we propose a novel approach to knowledge tracing that combines techniques from matrix factorization with recent progress in recurrent neural networks (RNNs) to effectively track the state of a student's knowledge.
Localized sketching for matrix multiplication and ridge regression
We consider sketched approximate matrix multiplication and ridge regression in the novel setting of localized sketching, where at any given point, only part of the data matrix is available.
Low-rank matrix completion and denoising under Poisson noise
This paper considers the problem of estimating a low-rank matrix from the observation of all or a subset of its entries in the presence of Poisson noise.
Active embedding search via noisy paired comparisons
Suppose that we wish to estimate a user's preference vector $w$ from paired comparisons of the form "does user $w$ prefer item $p$ or item $q$?," where both the user and items are embedded in a low-dimensional Euclidean space with distances that reflect user and item similarities.
As you like it: Localization via paired comparisons
Suppose that we wish to estimate a vector $\mathbf{x}$ from a set of binary paired comparisons of the form "$\mathbf{x}$ is closer to $\mathbf{p}$ than to $\mathbf{q}$" for various choices of vectors $\mathbf{p}$ and $\mathbf{q}$.
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint
Low-rank matrix factorizations arise in a wide variety of applications -- including recommendation systems, topic models, and source separation, to name just a few.
Constrained adaptive sensing
Suppose that we wish to estimate a vector $\mathbf{x} \in \mathbb{C}^n$ from a small number of noisy linear measurements of the form $\mathbf{y} = \mathbf{A x} + \mathbf{z}$, where $\mathbf{z}$ represents measurement noise.
Information Theory Information Theory
Manifold Based Dynamic Texture Synthesis from Extremely Few Samples
In this paper, we present a novel method to synthesize dynamic texture sequences from extremely few samples, e. g., merely two possibly disparate frames, leveraging both Markov Random Fields (MRFs) and manifold learning.
