2 citations · 2 across the 1 of their papers we have counts for
5 papers
QnAMaker: Data to Bot in 2 Minutes
Parag Agrawal, Tulasi Menon, Aya Kamel +15
Having a bot for seamless conversations is a much-desired feature that products and services today seek for their websites and mobile apps. These bots help reduce traffic received…
One Neuron to Fool Them All
Anshuman Suri, David Evans
Despite vast research in adversarial examples, the root causes of model susceptibility are not well understood. Instead of looking at attack-specific robustness, we propose a notio…
NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep
Parag Agrawal, Anshuman Suri
Existing Machine Learning techniques yield close to human performance on text-based classification tasks. However, the presence of multi-modal noise in chat data such as emoticons,…
A Trustworthy, Responsible and Interpretable System to Handle Chit Chat in Conversational Bots
Parag Agrawal, Anshuman Suri, Tulasi Menon
Most often, chat-bots are built to solve the purpose of a search engine or a human assistant: Their primary goal is to provide information to the user or help them complete a task.…
Hardening Deep Neural Networks via Adversarial Model Cascades
Deepak Vijaykeerthy, Anshuman Suri, Sameep Mehta +1
Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs. Works on securing neural networks against adversarial examples…