no code implementations • 10 Jun 2024 • Jinu Jayachandran, Muhammad Ubadah, Saif Khan Mohammed, Ronny Hadani, Ananthanarayanan Chockalingam, Robert Calderbank

The Zak-OTFS input/output (I/O) relation is predictable and non-fading when the delay and Doppler periods are greater than the effective channel delay and Doppler spreads, a condition which we refer to as the crystallization condition.

no code implementations • 4 May 2024 • Jinu Jayachandran, Rahul Kumar Jaiswal, Saif Khan Mohammed, Ronny Hadani, Ananthanarayanan Chockalingam, Robert Calderbank

We also demonstrate that, by limiting DD domain aliasing, Gaussian filters extend the region where the crystallization condition is satisfied.

no code implementations • 5 Apr 2024 • Muhammad Ubadah, Saif Khan Mohammed, Ronny Hadani, Shachar Kons, Ananthanarayanan Chockalingam, Robert Calderbank

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 ${\Lambda^*}$.

no code implementations • 17 Feb 2023 • Saif Khan Mohammed, Ronny Hadani, Ananthanarayanan Chockalingam, Robert Calderbank

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.

no code implementations • 17 Feb 2023 • Saif Khan Mohammed, Ronny Hadani, Ananthanarayanan Chockalingam, Robert Calderbank

Orthogonal time frequency space (OTFS) is a framework for communication and active sensing that processes signals in the delay-Doppler (DD) domain.

no code implementations • 17 Jul 2021 • Zhou Zhou, Lingjia Liu, Jiarui Xu, Robert Calderbank

Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain.

no code implementations • 24 Feb 2021 • Tefjol Pllaha, Olav Tirkkonen, Robert Calderbank

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

no code implementations • 22 Jan 2021 • Beyza Dabak, Ahmed Hareedy, Alexei Ashikhmin, Robert Calderbank

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

no code implementations • 30 Oct 2020 • Xinyu Tan, Narayanan Rengaswamy, Robert Calderbank

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

no code implementations • NeurIPS 2019 • Jie Ding, Robert Calderbank, Vahid Tarokh

Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information.

no code implementations • 25 Sep 2019 • Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng

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.

no code implementations • 24 Sep 2019 • Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng

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.

1 code implementation • 30 Jun 2019 • Narayanan Rengaswamy, Robert Calderbank, Swanand Kadhe, Henry D. Pfister

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

no code implementations • NAACL 2019 • Duc Minh Nguyen, Tien Huu Do, Robert Calderbank, Nikos Deligiannis

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.

no code implementations • 29 Jan 2019 • Duc Minh Nguyen, Robert Calderbank, Nikos Deligiannis

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.

no code implementations • 4 Jul 2018 • Duc Minh Nguyen, Evaggelia Tsiligianni, Robert Calderbank, Nikos Deligiannis

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.

no code implementations • ICLR 2019 • Xiuyuan Cheng, Qiang Qiu, Robert Calderbank, Guillermo Sapiro

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.

1 code implementation • 19 Mar 2018 • Narayanan Rengaswamy, Robert Calderbank, Swanand Kadhe, Henry D. Pfister

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

1 code implementation • ICML 2018 • Qiang Qiu, Xiuyuan Cheng, Robert Calderbank, Guillermo Sapiro

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.

1 code implementation • 27 Jan 2018 • Ahmad Beirami, Robert Calderbank, Mark Christiansen, Ken Duffy, Muriel Médard

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

no code implementations • CVPR 2018 • Wei Zhu, Qiang Qiu, Jiaji Huang, Robert Calderbank, Guillermo Sapiro, Ingrid Daubechies

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.

no code implementations • 11 Jul 2016 • Hugo Reboredo, Francesco Renna, Robert Calderbank, Miguel R. D. Rodrigues

This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements.

no code implementations • 8 May 2016 • Matthew Nokleby, Ahmad Beirami, Robert Calderbank

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.

no code implementations • 21 Dec 2015 • Jiaji Huang, Qiang Qiu, Robert Calderbank, Guillermo Sapiro

The new method encourages the relationships between the learned decisions to resemble a graph representing the manifold structure.

no code implementations • NeurIPS 2015 • Jiaji Huang, Qiang Qiu, Guillermo Sapiro, Robert Calderbank

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.

no code implementations • ICCV 2015 • Jiaji Huang, Qiang Qiu, Robert Calderbank, Guillermo Sapiro

Many recent efforts have been devoted to designing sophisticated deep learning structures, obtaining revolutionary results on benchmark datasets.

no code implementations • 7 Aug 2015 • Jure Sokolic, Francesco Renna, Robert Calderbank, Miguel R. D. Rodrigues

This paper considers the classification of linear subspaces with mismatched classifiers.

no code implementations • 15 Jul 2015 • Jiaji Huang, Qiang Qiu, Robert Calderbank

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.

no code implementations • 18 Dec 2014 • Qiang Qiu, Andrew Thompson, Robert Calderbank, Guillermo Sapiro

The Weyl transform is introduced as a rich framework for data representation.

no code implementations • 1 Dec 2014 • Francesco Renna, Liming Wang, Xin Yuan, Jianbo Yang, Galen Reeves, Robert Calderbank, Lawrence Carin, Miguel R. D. Rodrigues

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.

no code implementations • 26 Jan 2014 • Tong Wu, Gungor Polatkan, David Steel, William Brown, Ingrid Daubechies, Robert Calderbank

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.

no code implementations • NeurIPS 2013 • Liming Wang, David E. Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, Lawrence Carin

We consider design of linear projection measurements for a vector Poisson signal model.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.