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Haw-Shiuan Chang

UMass Amherst

21 papers hereh-index 161.4k citations34 works total

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

author position
  • first author10
  • middle author9
  • last author1

Across the 20 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CL16
  • cs.IR2
  • cond-mat.mtrl-sci1
  • cs.AI1
  • stat.ML1
affiliations
  • UMass Amherst
Homepage
same name
  • Haw-Shiuan Chang — 5 papers, h 1
  • Haw-Shiuan Chang — 2 papers, h 1

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
20172024
most citedActive Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples

129 citations · 275 across the 21 of their papers we have counts for

collaborators
Showing 2019Show all

3 papers · 1 filter

cs.CL2019★ 19 cited

Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition

Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan +2

Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, includin…

cs.CL2019★ 2 cited

The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures

Sheshera Mysore, Zach Jensen, Edward Kim +6

Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures…

cond-mat.mtrl-sci2019★ 10 cited

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Edward Kim, Zach Jensen, Alexander van Grootel +8

Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experime…

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