3 citations · 6 across the 3 of their papers we have counts for
4 papers
Don't Ask the LLM: Visual Token Pruning with Spectral Structure and Semantic Relevance
Intaek Shin, Geuntaek Lim, Sungjune Park +1
Large vision-language models (LVLMs) represent images and videos as long visual-token sequences, substantially increasing inference costs. These costs motivate visual-token pruning…
BankerToolBench: Evaluating AI Agents in End-to-End Investment Banking Workflows
Elaine Lau, Markus Dücker, Ronak Chaudhary +24
Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows. To evaluate frontier AI agents in a high-value, labor-intensive profe…
ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data
Ulyana Tkachenko, Aditya Thyagarajan, Jonas Mueller
Despite powering sensitive systems like autonomous vehicles, object detection remains fairly brittle in part due to annotation errors that plague most real-world training datasets.…
CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotators
Hui Wen Goh, Ulyana Tkachenko, Jonas Mueller
Real-world data for classification is often labeled by multiple annotators. For analyzing such data, we introduce CROWDLAB, a straightforward approach to utilize any trained classi…