63 citations · 244 across the 40 of their papers we have counts for
43 papers
High-Throughput LLM-Based Compression and What It Takes: A Systems Study of Inference Engines and Entropy Coding Optimizations
David Adams, Luisa Neubauer, Ping-Lin Kuo +5
Large language models are attractive text compressors because next-token probabilities can be converted into code lengths. In practice, however, the main bottleneck is often not mo…
ValueGround: Evaluating Culture-Conditioned Visual Value Grounding in MLLMs
Zhipin Wang, Christoph Leiter, Christian Frey +3
Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations of cultural values in language models…
When is Warmstarting Effective for Scaling Language Models?
Neeratyoy Mallik, Maciej Janowski, Johannes Hog +4
Model growth from a given checkpoint aims to accelerate training of a larger model, offering potential resource savings. Despite recent interest, warmstarting has seen limited prac…
Learning to Order: Task Sequencing as In-Context Optimization
Jan Kobiolka, Christian Frey, Arlind Kadra +2
Task sequencing (TS) is one of the core open problems in Deep Learning, arising in a plethora of real-world domains, from robotic assembly lines to autonomous driving. Unfortunatel…
POP: Prior-Fitted First-Order Optimization Policies
Jan Kobiolka, Christian Frey, Gresa Shala +3
Gradient-based optimizers are highly sensitive to design choices in their adaptive learning rate mechanisms. To address this limitation, we introduce POP, a meta-learned Reinforcem…
End-to-End Compression for Tabular Foundation Models
Guri Zabërgja, Rafiq Kamel, Arlind Kadra +2
The long-standing dominance of gradient-boosted decision trees for tabular data has recently been challenged by in-context learning tabular foundation models. In-context learning m…