Kilian Q. Weinberger

Kilian Q. Weinberger has 2 papers in our library, in 2018–2020, most of them on Sentiment & emotion, Multilingual & cross-lingual NLP; often with Arzoo Katiyar, Ben Athiwaratkun, Claire Cardie.

2 papers in our library · 2018–2020

current affiliationCornell University, Ithaca, United States

OpenAlex · ORCID · h-index 72 · 231 works · 81 851 citations OpenAlex, 2026-10-14

2 works in our library · 4 126 citations in our library · 4 more works in our library · ⭐ Paper Star rank #2371

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

Affiliations

Cornell University · Ithaca, United States — current · 2015–2026 on 95 papers · employment (Wikidata)
Washington University in St. Louis · St Louis, United States — 2010–2021 on 49 papers
Tsinghua University · Beijing, China — 2018 on 2 papers
Yahoo (United States) · Sunnyvale, United States — 2008–2009 on 9 papers
University of Pennsylvania · Philadelphia, United States — 2004–2007 on 9 papers · education until 2007 (Wikidata)
University of Oxford · Oxford, United Kingdom — education 1999–2002 (Wikidata)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

Topics

Sentiment & emotion1 Multilingual & cross-lingual NLP1

Works by year

2020s

20201 paper

2010s

20181 paper

Papers

The most cited first (OpenAlex and our library).

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification (2018 · 393 citations)
The paper addresses sentiment classification in languages with little or no annotated data. It transfers knowledge from labeled English (SOURCE) texts to a TARGET language that has only unlabeled text. The authors propose ADAN, which feeds a Deep Averaging Network (DAN) feature extractor into two branches: a…
Revisiting Few-sample BERT Fine-tuning (2020 · 361 citations)
Fine-tuning BERT on small datasets is notoriously unreliable. Runs that differ only in random seed can produce very different or even collapsed models, so practitioners often train many restarts and keep the best one. This study traces the instability to three widespread practices. First, BERTADAM, the default…

Also in our library

4 more works of theirs are in our library without a page here yet; they will appear as we publish them.

Co-authors here

8 co-authors with a page, the most papers together first.

Cited by

Papers of our library cite their works 4 126 times (of the 140 884 papers whose bibliographies we read); 284 citing papers with a page here →.

This page in Markdown → https://md.research.elamur.ai/free/en/authors/kilian-q-weinberger/

🧑‍🔬 Kilian Q. Weinberger