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Sandeep Subramanian

Nvidia

15 papers hereh-index 157.2k citations23 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author9

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CL12
  • cs.LG1
  • cs.NE1
  • stat.ML1
affiliations
  • Nvidia
Homepage
same name
  • Sandeep Subramanian — 8 papers, h 5
  • Sandeep Subramanian — 4 papers, h 3
  • Sandeep Subramanian — 2 papers, h 4
  • Sandeep Subramanian — 1 paper
  • Sandeep Subramanian — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162022
most citedA Deep Reinforcement Learning Chatbot

200 citations · 728 across the 14 of their papers we have counts for

collaborators
Showing 2017 · cs.CLShow all

4 papers · 2 filters

cs.CL2017★ 200 cited

A Deep Reinforcement Learning Chatbot

Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…

cs.CL2017★ 35 cited

Neural Models for Key Phrase Detection and Question Generation

Sandeep Subramanian, Tong Wang, Xingdi Yuan +3

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human…

cs.CL2017★ 101 cited

Adversarial Generation of Natural Language

Sai Rajeswar, Sandeep Subramanian, Francis Dutil +2

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the advers…

cs.CL2017

Machine Comprehension by Text-to-Text Neural Question Generation

Xingdi Yuan, Tong Wang, Caglar Gulcehre +5

We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervis…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.