Search Results for author: Robert Calderbank

Found 27 papers, 4 papers with code

Learning to Equalize OTFS

no code implementations17 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.

Binary Subspace Chirps

no code implementations24 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

Unequal Error Protection Achieves Threshold Gains on BEC and BSC via Higher Fidelity Messages

no code implementations22 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

Approximate Unitary 3-Designs from Transvection Markov Chains

no code implementations30 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

Gradient Information for Representation and Modeling

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.

Scale-Equivariant Neural Networks with Decomposed Convolutional Filters

no code implementations25 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.

Image Classification

Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters

no code implementations24 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.

Image Classification Translation

Logical Clifford Synthesis for Stabilizer Codes

1 code implementation30 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

Fake News Detection using Deep Markov Random Fields

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.

Fake News Detection

Geometric Matrix Completion with Deep Conditional Random Fields

no code implementations29 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.

Matrix Completion Recommendation Systems +1

Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning

no code implementations4 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.

Matrix Completion Multi-Task Learning

RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks

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.

Synthesis of Logical Clifford Operators via Symplectic Geometry

1 code implementation19 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

DCFNet: Deep Neural Network with Decomposed Convolutional Filters

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.

General Classification Image Classification

A Characterization of Guesswork on Swiftly Tilting Curves

1 code implementation27 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

LDMNet: Low Dimensional Manifold Regularized Neural Networks

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.

Face Recognition Small Data Image Classification

Bounds on the Number of Measurements for Reliable Compressive Classification

no code implementations11 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.

General Classification

Rate-Distortion Bounds on Bayes Risk in Supervised Learning

no code implementations8 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.

GraphConnect: A Regularization Framework for Neural Networks

no code implementations21 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.

Discriminative Robust Transformation Learning

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.

Geometry-aware Deep Transform

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.

Metric Learning

The Role of Principal Angles in Subspace Classification

no code implementations15 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.

General Classification

Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information

no code implementations1 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.

General Classification

Painting Analysis Using Wavelets and Probabilistic Topic Models

no code implementations26 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.

Topic Models

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