10 citations · 21 across the 16 of their papers we have counts for
11 papers · 1 filter
Balancing Multimodal Training Through Game-Theoretic Regularization
Konstantinos Kontras, Thomas Strypsteen, Christos Chatzichristos +3
Multimodal learning holds promise for richer information extraction by capturing dependencies across data sources. Yet, current training methods often underperform due to modality…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distribution
Wangduo Xie, Richard Schoonhoven, Tristan van Leeuwen +1
Computed tomography (CT) reconstruction plays a crucial role in industrial nondestructive testing and medical diagnosis. Sparse view CT reconstruction aims to reconstruct high-qual…
Diversity-Driven View Subset Selection for Indoor Novel View Synthesis
Zehao Wang, Han Zhou, Matthew B. Blaschko +2
Novel view synthesis of indoor scenes can be achieved by capturing a monocular video sequence of the environment. However, redundant information caused by artificial movements in t…
Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs
Enshu Liu, Junyi Zhu, Zinan Lin +6
The rapid advancement of large language models (LLMs) has led to architectures with billions to trillions of parameters, posing significant deployment challenges due to their subst…
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models
Junyi Zhu, Shuochen Liu, Yu Yu +6
Large language models (LLMs) excel in generating coherent text, but they often struggle with context awareness, leading to inaccuracies in tasks requiring faithful adherence to pro…
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
Xuefei Ning, Zifu Wang, Shiyao Li +7
Teaching to improve student models (e.g., knowledge distillation) is an extensively studied methodology in LLMs. However, for humans, teaching improves not only students but also t…
Improving Multimodal Learning with Multi-Loss Gradient Modulation
Konstantinos Kontras, Christos Chatzichristos, Matthew Blaschko +1
Learning from multiple modalities, such as audio and video, offers opportunities for leveraging complementary information, enhancing robustness, and improving contextual understand…
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction
Jiayang Shi, Junyi Zhu, Daniel M. Pelt +2
Computed Tomography (CT) is pivotal in industrial quality control and medical diagnostics. Sparse-view CT, offering reduced ionizing radiation, faces challenges due to its under-sa…
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
Enshu Liu, Junyi Zhu, Zinan Lin +8
Diffusion Models (DM) and Consistency Models (CM) are two types of popular generative models with good generation quality on various tasks. When training DM and CM, intermediate we…
Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis
Mingshi Li, Dusan Grujicic, Steven De Saeger +3
In recent years, machine learning has become crucial in remote sensing analysis, particularly in the domain of Land-use/Land-cover (LULC). The synergy of machine learning and satel…