Tootfinder

Op-Fed: Opinion, Stance, and Monetary Policy Annotations on FOMC Transcripts Using Active Learning
Alisa Kanganis, Katherine A. Keith
https://arxiv.org/abs/2509.13539 https://…

Op-Fed: Opinion, Stance, and Monetary Policy Annotations on FOMC Transcripts Using Active Learning
The U.S. Federal Open Market Committee (FOMC) regularly discusses and sets monetary policy, affecting the borrowing and spending decisions of millions of people. In this work, we release Op-Fed, a dataset of 1044 human-annotated sentences and their contexts from FOMC transcripts. We faced two major technical challenges in dataset creation: imbalanced classes -- we estimate fewer than 8% of sentences express a non-neutral stance towards monetary policy -- and inter-sentence dependence -- 65% of …

Towards Balanced Behavior Cloning from Imbalanced Datasets
Sagar Parekh, Heramb Nemlekar, Dylan P. Losey
https://arxiv.org/abs/2508.06319 https://arxiv.org…

Towards Balanced Behavior Cloning from Imbalanced Datasets
Robots should be able to learn complex behaviors from human demonstrations. In practice, these human-provided datasets are inevitably imbalanced: i.e., the human demonstrates some subtasks more frequently than others. State-of-the-art methods default to treating each element of the human's dataset as equally important. So if -- for instance -- the majority of the human's data focuses on reaching a goal, and only a few state-action pairs move to avoid an obstacle, the learning algorithm will pla…

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining
Prashant Singh Basnet, Roshan Chitrakar
https://arxiv.org/abs/2509.08586 https://

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining
This research explored the hybridization of CNN and ViT within a training dataset of limited size, and introduced a distinct class imbalance. The training was made from scratch with a mere focus on theoretically and experimentally exploring the architectural strengths of the proposed hybrid model. Experiments were conducted across varied data fractions with balanced and imbalanced training datasets. Comparatively, the hybrid model, complementing the strengths of CNN and ViT, achieved the highes…

Improving Survival Models in Healthcare by Balancing Imbalanced Cohorts: A Novel Approach
Catherine Ning, Dimitris Bertsimas, Johan Gagni\`ere, Stefan Buettner, Per Eystein Loenning, Hideo Baba, Itaru Endo, Georgios Stasinos, Richard Burkhart, Federico N. Auecio, Felix Balzer, Cornelis Verhoef, Martin E. Kreis, Georgios Antonios Margonis
https://

Improving Survival Models in Healthcare by Balancing Imbalanced Cohorts: A Novel Approach
We explore whether survival model performance in underrepresented high- and low-risk subgroups - regions of the prognostic spectrum where clinical decisions are most consequential - can be improved through targeted restructuring of the training dataset. Rather than modifying model architecture, we propose a novel risk-stratified sampling method that addresses imbalances in prognostic subgroup density to support more reliable learning in underrepresented tail strata. We introduce a novel methodo…

GenCAD-3D: CAD Program Generation using Multimodal Latent Space Alignment and Synthetic Dataset Balancing
Nomi Yu (Massachusetts Institute of Technology), Md Ferdous Alam (Massachusetts Institute of Technology), A. John Hart (Massachusetts Institute of Technology), Faez Ahmed (Massachusetts Institute of Technology)
https://arxiv.org/abs/25…

GenCAD-3D: CAD Program Generation using Multimodal Latent Space Alignment and Synthetic Dataset Balancing
CAD programs, structured as parametric sequences of commands that compile into precise 3D geometries, are fundamental to accurate and efficient engineering design processes. Generating these programs from nonparametric data such as point clouds and meshes remains a crucial yet challenging task, typically requiring extensive manual intervention. Current deep generative models aimed at automating CAD generation are significantly limited by imbalanced and insufficiently large datasets, particularl…

Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning
Abdul Hannan, Zahid Mahmood, Rizwan Qureshi, Hazrat Ali
https://arxiv.org/abs/2507.19199

Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning
Automatic classification of Diabetic Retinopathy (DR) can assist ophthalmologists in devising personalized treatment plans, making it a critical component of clinical practice. However, imbalanced data distribution in the dataset becomes a bottleneck in the generalization of deep learning models trained for DR classification. In this work, we combine global attention block (GAB) and category attention block (CAB) into the deep learning model, thus effectively overcoming the imbalanced data dist…