5 citations · 5 across the 12 of their papers we have counts for
13 papers
Not Another Text Benchmark: Putting the "Visual" Back in Visual Question Answering for Large Video Models
Rwiddhi Chakraborty, Yinong, Yinong Oliver Wang +9
Large video models have exhibited impressive performance on a wide range of visual question answering tasks, owing to the rise of powerful, pretrained text and vision encoders. The…
SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis
Yuqing Yang, Alexander Schmatz, Zhaozhao Ma +3
Explainability is increasingly seen as a crucial requirement in AI-based medical diagnosis, particularly in safety-critical clinical decision-making. Most existing explainability m…
A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen +5
Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains un…
OVA-IB: One vs All Information Bottleneck for Multi-Modal Alignment
Tianchao Li, Shujian Yu, Xinrui Zu +4
Contrastive learning is effective for aligning paired views or modalities, but alignment beyond two modalities remains non-trivial and comparatively underexplored. Pairwise CLIP-st…
NOFE - Neural Operator Function Embedding
Lars Uebbing, Harald L. Joakimsen, Siyan Chen +6
Most dimensionality reduction methods treat data as discrete point clouds, ignoring the continuous domain structure inherent to many real-world processes. To bridge this gap, we in…
WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
Yi Xiao, Qilong Jia, Hang Fan +4
Many decisions in complex terrain require fast wind estimates at specific locations and heights rather than dense forecasts on fixed grids. We present WindINR, a latent-state impli…
Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation
Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya +3
Contrast-enhanced Computed Tomography (CT) is important for diagnosis and treatment planning for various medical conditions. Deep learning (DL) based segmentation models may enable…
From Colors to Classes: Emergence of Concepts in Vision Transformers
Teresa Dorszewski, Lenka Tětková, Robert Jenssen +2
Vision Transformers (ViTs) are increasingly utilized in various computer vision tasks due to their powerful representation capabilities. However, it remains understudied how ViTs p…
REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
Kristoffer K. Wickstrøm, Thea Brüsch, Michael C. Kampffmeyer +1
Incorporating uncertainty is crucial to provide trustworthy explanations of deep learning models. Recent works have demonstrated how uncertainty modeling can be particularly import…
FLEXtime: Filterbank learning to explain time series
Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt +2
State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are diff…
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications
Mingfei Lu, Chenxu Li, Shujian Yu +2
Divergence measures play a central role and become increasingly essential in deep learning, yet efficient measures for multiple (more than two) distributions are rarely explored. T…
FreqRISE: Explaining time series using frequency masking
Thea Brüsch, Kristoffer Knutsen Wickstrøm, Mikkel N. Schmidt +2
Time-series data are fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision…
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya +2
Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in m…