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

Rayyan Abdalla

dblp:336/7729 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 · 75% Vision and language · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning › model quantization
binary weight quantization		 1.012026
Bi-VLM: Binary Post-Training Quantization for Vision-Language Models · AAAI 2026
Machine learning › Efficient and distributed learning
model compression		 1.012026
Bi-VLM: Binary Post-Training Quantization for Vision-Language Models · AAAI 2026
Machine learning › Efficient and distributed learning › model compression › quantization
post-training quantization		 1.012026
Bi-VLM: Binary Post-Training Quantization for Vision-Language Models · AAAI 2026
Computer vision › Vision and language
vision-language model		 1.012026
Bi-VLM: Binary Post-Training Quantization for Vision-Language Models · AAAI 2026

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

post-training quantization · 1.0
YearPublicationVenuePosition
2026 Bi-VLM: Binary Post-Training Quantization for Vision-Language Models
abstract
We address the critical gap between the computational demands of vision-language models and the possible ultra-low-bit weight precision (bitwidth
Xijun Wang 0002, Rayyan Abdalla, Junyun Huang, Ruiqi Xian, Dinesh Manocha
 AAAI2