Search Results for author: Sreeram Kannan

Found 24 papers, 10 papers with code

Deepcode and Modulo-SK are Designed for Different Settings

no code implementations18 Aug 2020 Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

DeepCode is designed and evaluated for the AWGN channel with (potentially delayed) uncoded output feedback.

C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation

no code implementations17 May 2020 Arnab Kumar Mondal, Arnab Bhattacharya, Sudipto Mukherjee, Prathosh AP, Sreeram Kannan, Himanshu Asnani

Estimation of information theoretic quantities such as mutual information and its conditional variant has drawn interest in recent times owing to their multifaceted applications.

Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

1 code implementation NeurIPS 2019 Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications.

Improving Federated Learning Personalization via Model Agnostic Meta Learning

2 code implementations27 Sep 2019 Yihan Jiang, Jakub Konečný, Keith Rush, Sreeram Kannan

We present FL as a natural source of practical applications for MAML algorithms, and make the following observations.

Federated Learning Meta-Learning

Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples

no code implementations28 Jul 2019 Hossein Hosseini, Sreeram Kannan, Radha Poovendran

In this paper, we first develop a classifier-based adaptation of the statistical test method and show that it improves the detection performance.

Learning in Gated Neural Networks

no code implementations6 Jun 2019 Ashok Vardhan Makkuva, Sewoong Oh, Sreeram Kannan, Pramod Viswanath

Gating is a key feature in modern neural networks including LSTMs, GRUs and sparsely-gated deep neural networks.

CCMI : Classifier based Conditional Mutual Information Estimation

no code implementations5 Jun 2019 Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan

Conditional Mutual Information (CMI) is a measure of conditional dependence between random variables X and Y, given another random variable Z.

Mutual Information Estimation Time Series +1

DeepTurbo: Deep Turbo Decoder

1 code implementation6 Mar 2019 Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

We focus on Turbo codes and propose DeepTurbo, a novel deep learning based architecture for Turbo decoding.

BOAssembler: a Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance

1 code implementation14 Feb 2019 Shunfu Mao, Yihan Jiang, Edwin Basil Mathew, Sreeram Kannan

High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample.

LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks

1 code implementation30 Nov 2018 Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards.

PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously

no code implementations27 Sep 2018 Songze Li, Mingchao Yu, Chien-Sheng Yang, A. Salman Avestimehr, Sreeram Kannan, Pramod Viswanath

In particular, we propose PolyShard: ``polynomially coded sharding'' scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system.

Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory Information Theory

ClusterGAN : Latent Space Clustering in Generative Adversarial Networks

7 code implementations10 Sep 2018 Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, Sreeram Kannan

While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not retained in the GAN latent space.

Deepcode: Feedback Codes via Deep Learning

1 code implementation NeurIPS 2018 Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications.

Mimic and Classify : A meta-algorithm for Conditional Independence Testing

1 code implementation25 Jun 2018 Rajat Sen, Karthikeyan Shanmugam, Himanshu Asnani, Arman Rahimzamani, Sreeram Kannan

Given independent samples generated from the joint distribution $p(\mathbf{x},\mathbf{y},\mathbf{z})$, we study the problem of Conditional Independence (CI-Testing), i. e., whether the joint equals the CI distribution $p^{CI}(\mathbf{x},\mathbf{y},\mathbf{z})= p(\mathbf{z}) p(\mathbf{y}|\mathbf{z})p(\mathbf{x}|\mathbf{z})$ or not.

Communication Algorithms via Deep Learning

3 code implementations ICLR 2018 Hyeji Kim, Yihan Jiang, Ranvir Rana, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms).

Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms

no code implementations21 Feb 2018 Ashok Vardhan Makkuva, Sewoong Oh, Sreeram Kannan, Pramod Viswanath

Once the experts are known, the recovery of gating parameters still requires an EM algorithm; however, we show that the EM algorithm for this simplified problem, unlike the joint EM algorithm, converges to the true parameters.

Ensemble Learning

Potential Conditional Mutual Information: Estimators, Properties and Applications

no code implementations13 Oct 2017 Arman Rahimzamani, Sreeram Kannan

We define the potential conditional mutual information as the conditional mutual information calculated with a modified joint distribution p_{Y|X, Z} q_{X, Z}, where q_{X, Z} is a potential distribution, fixed airport.

Time Series

Estimating Mutual Information for Discrete-Continuous Mixtures

1 code implementation NeurIPS 2017 Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

We provide numerical experiments suggesting superiority of the proposed estimator compared to other heuristics of adding small continuous noise to all the samples and applying standard estimators tailored for purely continuous variables, and quantizing the samples and applying standard estimators tailored for purely discrete variables.

Mutual Information Estimation

Blocking Transferability of Adversarial Examples in Black-Box Learning Systems

no code implementations13 Mar 2017 Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang, Radha Poovendran

Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars.

Medical Diagnosis

Deceiving Google's Perspective API Built for Detecting Toxic Comments

no code implementations27 Feb 2017 Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran

In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples.

Learning Temporal Dependence from Time-Series Data with Latent Variables

no code implementations27 Aug 2016 Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran

We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes.

Time Series

Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications

no code implementations10 Feb 2016 Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

We conduct an axiomatic study of the problem of estimating the strength of a known causal relationship between a pair of variables.

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