Tootfinder

CLIP-HandID: Vision-Language Model for Hand-Based Person Identification
Nathanael L. Baisa, Babu Pallam, Amudhavel Jayavel
https://arxiv.org/abs/2506.12447

CLIP-HandID: Vision-Language Model for Hand-Based Person Identification
This paper introduces a new approach to person identification based on hand images, designed specifically for criminal investigations. The method is particularly valuable in serious crimes like sexual abuse, where hand images are often the sole identifiable evidence available. Our proposed method, CLIP-HandID, leverages pre-trained foundational vision-language model, particularly CLIP, to efficiently learn discriminative deep feature representations from hand images given as input to the image …

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems
Chuangji Meng, Jinghuai Gao, Wenting Shang, Yajun Tian, Hongling Chen, Tieqiang Zhang, Zongben Xu
https://arxiv.org/abs/2506.12897

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems
Geophysical inverse problems are often ill-posed and admit multiple solutions. Conventional discriminative methods typically yield a single deterministic solution, which fails to model the posterior distribution, cannot generate diverse high-quality stochastic solutions, and limits uncertainty quantification. Addressing this gap, we propose an unsupervised posterior sampling method conditioned on the noisy observations and the inverse problem, eliminating the need to retrain a task-specific con…

Scaling Transformers for Discriminative Recommendation via Generative Pretraining
Chunqi Wang, Bingchao Wu, Zheng Chen, Lei Shen, Bing Wang, Xiaoyi Zeng
https://arxiv.org/abs/2506.03699

Scaling Transformers for Discriminative Recommendation via Generative Pretraining
Discriminative recommendation tasks, such as CTR (click-through rate) and CVR (conversion rate) prediction, play critical roles in the ranking stage of large-scale industrial recommender systems. However, training a discriminative model encounters a significant overfitting issue induced by data sparsity. Moreover, this overfitting issue worsens with larger models, causing them to underperform smaller ones. To address the overfitting issue and enhance model scalability, we propose a framework na…

PixCell: A generative foundation model for digital histopathology images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li, Kostas Triaridis, Varun Belagali, Saarthak Kapse, Tarak Nath Nandi, Ravi K Madduri, Prateek Prasanna, Tahsin Kurc, Rajarsi R. Gupta, Joel Saltz, Dimitris Samaras
https://arxiv.org/abs/2506.05127

PixCell: A generative foundation model for digital histopathology images
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Contrastive self-supervised and vision-language models have been shown to effectively mine large pathology datasets to learn discriminative representations. On the other hand, generative models, capable of synthesizing realistic and diverse images, present a compelling solution to address unique problems in pathology that involve synthesizing images; overcoming annota…

AI-Driven SEEG Channel Ranking for Epileptogenic Zone Localization
Saeed Hashemi, Genchang Peng, Mehrdad Nourani, Omar Nofal, Jay Harvey
https://arxiv.org/abs/2506.09255

AI-Driven SEEG Channel Ranking for Epileptogenic Zone Localization
Stereo-electroencephalography (SEEG) is an invasive technique to implant depth electrodes and collect data for pre-surgery evaluation. Visual inspection of signals recorded from hundreds of channels is time consuming and inefficient. We propose a machine learning approach to rank the impactful channels by incorporating clinician's selection and computational finding. A classification model using XGBoost is trained to learn the discriminative features of each channel during ictal periods. Then, …

Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models
Christian Marius Lillelund, Shi-ang Qi, Russell Greiner, Christian Fischer Pedersen
https://arxiv.org/abs/2506.02075

Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models
We argue that many survival analysis and time-to-event models are incorrectly evaluated. First, we survey many examples of evaluation approaches in the literature and find that most rely on concordance (C-index). However, the C-index only measures a model's discriminative ability and does not assess other important aspects, such as the accuracy of the time-to-event predictions or the calibration of the model's probabilistic estimates. Next, we present a set of key desiderata for choosing the ri…