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Anuj Sharma

Anuj Sharma has 2 papers in our library, in 2026, most of them on Language models & pretraining.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 32 · 135 works · 4,923 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11435

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

🎓National Institute of Technology DelhiNew Delhi · India2014–2026 · 75 papersEmployment since 2014current🎓Indian Institute of Technology DelhiNew Delhi · India2004–2008 · 17 papersEmployment 2006–2007🎓Gautam Buddha UniversityGreater Noida · India2010–2014 · 5 papersEmployment 2010–2014🎓Dehradun Institute of Technology UniversityDehradun · India2015–2020 · 4 papers🎓Gurukul Kangri VishwavidyalayaHaridwar · India2022–2024 · 2 papers🎓Friedrich Schiller University JenaJena · Germany2007–2009 · 2 papersEmployment 2007–2008🏢ASMLVeldhoven · Netherlands2008–2010Employment 2008–2010
🏛️Indian Institutes of TechnologyIndia2002–2006Education 2002–2006
🎓Indian Institute of Technology RoorkeeRoorkee · India1999–2001Education 1999–2001

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Latent semantic indexing · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics (2026 · 1 citation)
    Post-training activation compression helps fit LLMs onto limited hardware. Standard SVD-style methods keep the directions with the largest activation variance and ignore how strongly the loss depends on each direction. The authors argue that this harms factual knowledge, which often sits in low-variance but…
  2. FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation (2026)
    The paper brings Representation Fine-Tuning (ReFT) into federated learning. ReFT adapts a frozen model by editing its hidden activations, not its weights. The federated setting is hard because clients pursue different tasks, and simply averaging representation edits with FedAvg can break their semantic alignment.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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