Search Results for author:
Found 25 papers, 11 papers with code
HQAlign: Aligning nanopore reads for SV detection using current-level modeling
Moreover, HQAlign improves the alignment rate to 89. 35% from minimap2 85. 64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86. 65% from 83. 48% for nanopore reads alignment to GRCh37 human genome.
Deepcode and Modulo-SK are Designed for Different Settings
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
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
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
We present FL as a natural source of practical applications for MAML algorithms, and make the following observations.
Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples
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
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
Conditional Mutual Information (CMI) is a measure of conditional dependence between random variables X and Y, given another random variable Z.
Dropping Pixels for Adversarial Robustness
Deep neural networks are vulnerable against adversarial examples.
DeepTurbo: Deep Turbo Decoder
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
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
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
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
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
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
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
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
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.
Potential Conditional Mutual Information: Estimators, Properties and Applications
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.
Estimating Mutual Information for Discrete-Continuous Mixtures
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.
Discovering Potential Correlations via Hypercontractivity
Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest.
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars.
Deceiving Google's Perspective API Built for Detecting Toxic Comments
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
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.
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
We conduct an axiomatic study of the problem of estimating the strength of a known causal relationship between a pair of variables.
