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Demo Data from EDBT 2026 / reviewers in the wild / expert

Scott Mahlke

dblp:429/0057 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-0438-0616ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Efficient and distributed learning · 50% Language models and text generation · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
large language model training		 1.012026
SNIP: An Adaptive Mixed Precision Framework for Subbyte Large Language Model Training · ASPLOS (2) 2026
Machine learning › Efficient and distributed learning
low-precision training		 1.012026
SNIP: An Adaptive Mixed Precision Framework for Subbyte Large Language Model Training · ASPLOS (2) 2026

Methods — techniques the papers use, named apart from their topics

quantization · 1.0mixed-precision training · 1.0
YearPublicationVenuePosition
2026 SNIP: An Adaptive Mixed Precision Framework for Subbyte Large Language Model Training
abstract
Training large language models (LLMs) efficiently while preserving model quality poses significant challenges, particularly with subbyte precision supported by state-of-the-art GPUs. Current mixed-precision training approaches either apply uniform precision to all GEMM operations or rely on heuristic-based methods that fail to generalize during training, leading to suboptimal convergence and instability.
Yunjie Pan, Yongyi Yang, Hanmei Yang, Scott Mahlke
 ASPLOS (2)4