I am broadly interested in Natural Language Processing, Speech Recognition and Machine Learning Security & Privacy.
Thieves on Sesame Street! Model Extraction of BERT-based APIs
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer
blog // project page (code + slides + Twitter + external coverage)
Syntactically Supervised Transformers for Faster Neural Machine Translation
Nader Akoury, Kalpesh Krishna, Mohit Iyyer
code // poster
Trick or TReAT: Thematic Reinforcement for Artistic Typography
Purva Tendulkar, Kalpesh Krishna, Ramprasaath R. Selvaraju, Devi Parikh
ICCC 2019 (oral presentation, Best Presentation Award)
code // slides // video // demo
Revisiting the Importance of Encoding Logic Rules in Sentiment Classification
Kalpesh Krishna, Preethi Jyothi, Mohit Iyyer
EMNLP 2018 (oral presentation, short paper)
code + data // slides // video
Hierarchical Multitask Learning for CTC-based Speech Recognition
Kalpesh Krishna, Shubham Toshniwal, Karen Livescu
A Study of All-Convolutional Encoders for Connectionist Temporal Classification
Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu
ICASSP 2018 (Awarded SPS Travel Grant)
Collaborators (in order of publication date): Karen Livescu, Kevin Gimpel, Liang Lu, Shubham Toshniwal, Preethi Jyothi, Mohit Iyyer, Purva Tendulkar, Ramprasaath R. Selvaraju, Devi Parikh, Nader Akoury, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot
SunPy: A Python package for Solar Physics
Stuart J. Mumford and others
Other Research (Course Projects)
MixMatch on Vision + Language Tasks (NLVR2): An attempt to integrate the MixMatch data augmentation algorithm for semi-supervised image classification to the challenging setting of NLVR2, where the input space has both images and text (report).
Research Exchange - A Collaborative Paper Annotation Tool - A platform to collaboratively annotate scientific literature to help newcomers understand research papers, built during an Human Computer Interaction course project (report).
Inference Networks for Structured Prediction - A TensorFlow implementation for the multi-label classification experiments in Learning Approximate Inference Networks for Structured Prediction. Also contains experiments on the FIGMENT dataset and a extension to Inference Network training algorithm based on Wasserstein GANs (report).
Diversity Sampling in Machine Learning - An implementation of Diverse Beam Search for Neural Networks in Language Modelling. Also contains the original (slightly modified code) for the interactive segmentation experiments in Diverse M-Best Solutions in MRFs (report).
Macro Actions in Reinforcement Learning - A suite of five algorithms (including ideas from “Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning”) encouraging agents to repeat actions (report).
Single Image Haze Removal - An implementation of He et al. 2009, “Single Image Haze Removal using Dark Channel Prior” and an ongoing implementation of Bahat & Irani 2016, “Blind Dehazing using Internal Patch Recurrence” (report).
CNNs for Sentence Classification - A TensorFlow 1.1 implementation of Kim 2014, “Convolutional Neural Networks for Sentence Classification”.
Brittle Fracture Simulation - Python implementation of O’Brien and Hodgins 1999, “Graphical Modeling and Animation of Brittle Fracture”.
ECG Signal Analysis - Python implementation of parts of Christopher Buck, Aneesh Sampath 2013, “ECG Signal Analysis for Myocardial Infarction Detection.”.
Indian Language Datasets
As a part of my RnD project at IIT Bombay, I am releasing the dataset used to train my neural network language models. These have been mined from Wikipedia and I hope this will help further research in language modelling for Indian morphologically rich languages. The folder also contains the original PTB dataset.
- Malayalam (denoted by
- Tamil (denoted by
- Kannada (denoted by
- Telugu (denoted by
- Hindi (denoted by
- PTB (denoted by
All these datasets are compatible with SRILM. Files marked with
unk have replaced all singletons with
<unk> tokens. Files marked with
char are character versions. All datasets have a
test file. You will find the dataset here.