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Tong Wang

26 papers hereh-index 191.6k citations40 works total

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

author position
  • sole author2
  • first author9
  • middle author11
  • last author3

Across the 25 of 26 papers where every author was matched, so the position is known.

fields
  • cs.LG11
  • cs.CL7
  • cs.AI4
  • cs.NE1
  • eess.AS1
  • stat.AP1
same name
  • Tong Wang — 17 papers, h 5
  • Tong Wang — 11 papers, h 4
  • Tong Wang — 8 papers, h 14
  • Tong Wang — 7 papers, h 12
  • Tong Wang — 7 papers, h 14
  • Tong Wang — 7 papers, h 14

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
20152022
most citedGPT-3-driven pedagogical agents for training children's curious question-asking skills

122 citations · 553 across the 26 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.LG2017★ 106 cited

Rapid Adaptation with Conditionally Shifted Neurons

Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri +1

We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionall…

cs.AI2017★ 2 cited

Multi-Value Rule Sets

Tong Wang

We present the Multi-vAlue Rule Set (MARS) model for interpretable classification with feature efficient presentations. MARS introduces a more generalized form of association rules…

cs.AI2017★ 6 cited

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect

Tong Wang, Cynthia Rudin

A key question in causal inference analyses is how to find subgroups with elevated treatment effects. This paper takes a machine learning approach and introduces a generative model…

cs.CL2017★ 83 cited

A Joint Model for Question Answering and Question Generation

Tong Wang, Xingdi Yuan, Adam Trischler

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework tha…

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