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Found 30 papers, 4 papers with code
Designed Measurements for Vector Count Data
We consider design of linear projection measurements for a vector Poisson signal model.
Painting Analysis Using Wavelets and Probabilistic Topic Models
In this paper, computer-based techniques for stylistic analysis of paintings are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto di Bondone.
Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information
These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of linear features extracted from the signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay.
Data Representation using the Weyl Transform
The Weyl transform is introduced as a rich framework for data representation.
The Role of Principal Angles in Subspace Classification
Subspace models play an important role in a wide range of signal processing tasks, and this paper explores how the pairwise geometry of subspaces influences the probability of misclassification.
Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification
This paper considers the classification of linear subspaces with mismatched classifiers.
Geometry-aware Deep Transform
Many recent efforts have been devoted to designing sophisticated deep learning structures, obtaining revolutionary results on benchmark datasets.
Discriminative Robust Transformation Learning
This paper proposes a framework for learning features that are robust to data variation, which is particularly important when only a limited number of trainingsamples are available.
GraphConnect: A Regularization Framework for Neural Networks
The new method encourages the relationships between the learned decisions to resemble a graph representing the manifold structure.
Rate-Distortion Bounds on Bayes Risk in Supervised Learning
We provide lower and upper bounds on the rate-distortion function, using $L_p$ loss as the distortion measure, of a maximum a priori classifier in terms of the differential entropy of the posterior distribution and a quantity called the interpolation dimension, which characterizes the complexity of the parametric distribution family.
Bounds on the Number of Measurements for Reliable Compressive Classification
This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements.
LDMNet: Low Dimensional Manifold Regularized Neural Networks
To resolve this, we propose a new framework, the Low-Dimensional-Manifold-regularized neural Network (LDMNet), which incorporates a feature regularization method that focuses on the geometry of both the input data and the output features.
A Characterization of Guesswork on Swiftly Tilting Curves
We show that the tilt operation on a memoryless string-source parametrizes an exponential family of memoryless string-sources, which we refer to as the tilted family.
Information Theory Information Theory
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
In this paper, we suggest to decompose convolutional filters in CNN as a truncated expansion with pre-fixed bases, namely the Decomposed Convolutional Filters network (DCFNet), where the expansion coefficients remain learned from data.
Synthesis of Logical Clifford Operators via Symplectic Geometry
We propose a mathematical framework for synthesizing physical circuits that implement logical Clifford operators for stabilizer codes.
Information Theory Information Theory Quantum Physics 15Axx, 15B10, 20D45, 51A50, 68R01, 68W01, 81R05, 94B05
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks.
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning
Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task.
Geometric Matrix Completion with Deep Conditional Random Fields
We consider matrix completion as a structured prediction problem in a conditional random field (CRF), which is characterized by a maximum a posterior (MAP) inference, and we propose a deep model that predicts the missing entries by solving the MAP inference problem.
Fake News Detection using Deep Markov Random Fields
While the correlations among news articles have been shown to be effective cues for online news analysis, existing deep-learning-based methods often ignore this information and only consider each news article individually.
Logical Clifford Synthesis for Stabilizer Codes
Furthermore, we show that any circuit that normalizes the stabilizer of the code can be transformed into a circuit that centralizes the stabilizer, while realizing the same logical operation.
Quantum Physics Information Theory Information Theory
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when dealing with multiscale inputs.
Scale-Equivariant Neural Networks with Decomposed Convolutional Filters
Encoding the input scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many vision tasks especially when dealing with multiscale input signals.
Gradient Information for Representation and Modeling
Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information.
Approximate Unitary 3-Designs from Transvection Markov Chains
We construct a graph whose vertices are Pauli matrices, and two vertices are connected by directed edges if and only if they commute.
Quantum Physics
Unequal Error Protection Achieves Threshold Gains on BEC and BSC via Higher Fidelity Messages
Results show that UEP via higher fidelity parity bits achieves up to about $17\%$ and $28\%$ threshold gains on BEC and BSC, respectively.
Information Theory Signal Processing Information Theory
Binary Subspace Chirps
We describe in details the interplay between binary symplectic geometry and quantum computation, with the ultimate goal of constructing highly structured codebooks.
Information Theory Information Theory
Learning to Equalize OTFS
Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain.
OTFS -- Predictability in the Delay-Doppler Domain and its Value to Communication and Radar Sensing
This reconstruction formula makes it possible to study predictability of the Zak-OTFS I/O relation for a sampled system that operates under finite duration and bandwidth constraints.
OTFS -- A Mathematical Foundation for Communication and Radar Sensing in the Delay-Doppler Domain
Orthogonal time frequency space (OTFS) is a framework for communication and active sensing that processes signals in the delay-Doppler (DD) domain.
Zak-OTFS for Integration of Sensing and Communication
The self-ambiguity function of the point pulsone is supported on the period lattice ${\Lambda}_{p}$, and by applying a discrete chirp filter, we obtain a spread pulsone with a self-ambiguity function that is supported on a rotated lattice ${\Lambda^*}$.
