1 code implementation • 23 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.
no code implementations • 28 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.
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.
3 code implementations • 3 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.
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.
no code implementations • 22 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.
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.
no code implementations • 5 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.