Search Results for author: Mohit Yadav

Found 8 papers, 3 papers with code

Kernel Interpolation with Sparse Grids

1 code implementation23 May 2023 Mohit Yadav, Daniel Sheldon, Cameron Musco

Structured kernel interpolation (SKI) accelerates Gaussian process (GP) inference by interpolating the kernel covariance function using a dense grid of inducing points, whose corresponding kernel matrix is highly structured and thus amenable to fast linear algebra.

Faster Kernel Interpolation for Gaussian Processes

no code implementations28 Jan 2021 Mohit Yadav, Daniel Sheldon, Cameron Musco

Structured kernel interpolation (SKI) is among the most scalable methods: by placing inducing points on a dense grid and using structured matrix algebra, SKI achieves per-iteration time of O(n + m log m) for approximate inference.

Gaussian Processes regression

Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders

1 code implementation NAACL 2019 Andrew Drozdov, Patrick Verga, Mohit Yadav, Mohit Iyyer, Andrew McCallum

We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree.

Constituency Grammar Induction Sentence

Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders

3 code implementations3 Apr 2019 Andrew Drozdov, Pat Verga, Mohit Yadav, Mohit Iyyer, Andrew McCallum

We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree.

Constituency Parsing Sentence +1

Information Bottleneck Inspired Method For Chat Text Segmentation

no code implementations IJCNLP 2017 S Vishal, Mohit Yadav, Lovekesh Vig, Gautam Shroff

We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints.

Representation Learning Text Generation +2

Deep Triphone Embedding Improves Phoneme Recognition

no code implementations22 Oct 2017 Mohit Yadav, Vivek Tyagi

DTEs are generated using a four hidden layer DNN with 3000 nodes in each hidden layer at the first-stage.

Dimensionality Reduction General Classification

Learning and Knowledge Transfer with Memory Networks for Machine Comprehension

no code implementations EACL 2017 Mohit Yadav, Lovekesh Vig, Gautam Shroff

Motivated by these practical issues, we propose a novel curriculum inspired training procedure for Memory Networks to improve the performance for machine comprehension with relatively small volumes of training data.

Question Answering Reading Comprehension +1

ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines

no code implementations5 May 2016 Mohit Yadav, Pankaj Malhotra, Lovekesh Vig, K Sriram, Gautam Shroff

The available data is then augmented with data generated from the ODE, and the anomaly detector is retrained on this augmented dataset.

Anomaly Detection Time Series +1

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