#probabilistic modeling
11 resultsVisual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
Tianfan Xue, Jiajun Wu, Katherine L. Bouman +1
The paper introduces a probabilistic model using a Cross Convolutional Network to generate multiple plausible future video frames from a single input image.
Random Sum-Product Forests with Residual Links
Fabrizio Ventola, Karl Stelzner, Alejandro Molina +1
The paper proposes Random Sum-Product Forests (RSPFs), an ensemble of randomly generated sum‑product networks, and introduces residual links that let components share specialized s…
Probabilistic Face Embeddings
Yichun Shi, Anil K. Jain
The paper introduces Probabilistic Face Embeddings, which model each face image as a Gaussian distribution in a latent space to capture both feature estimates and their uncertainty…
Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers
Samuel Budd, Matthew Sinclair, Bishesh Khanal +6
The paper presents a probabilistic deep learning system that provides real-time feedback on the confidence of fetal head circumference measurements from ultrasound, helping sonogra…
Maximum likelihood convolutional beamformer for simultaneous denoising and dereverberation
Tomohiro Nakatani, Keisuke Kinoshita
The paper proposes a probabilistic formulation of the Weighted Power minimization Distortionless response (WPD) convolutional beamformer, unifying weighted prediction error derever…
Probabilistic Permutation Invariant Training for Speech Separation
Midia Yousefi, Soheil Khorram, John H. L. Hansen
The paper introduces Probabilistic Permutation Invariant Training (Prob‑PIT), a method that treats the output‑label assignment as a latent random variable to improve speaker‑indepe…
Constructing High Precision Knowledge Bases with Subjective and Factual Attributes
Ari Kobren, Pablo Barrio, Oksana Yakhnenko +2
The paper proposes a probabilistic consensus modeling approach, using neural networks, to build knowledge bases that store both factual and subjective attributes while allowing the…
Conditional Generative Neural System for Probabilistic Trajectory Prediction
Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
The paper introduces a conditional generative neural system that predicts multiple plausible future trajectories for dynamic agents by modeling the distribution of possible motions…
Probabilistic Models of Relational Implication
Xavier Holt
The paper introduces a principled probabilistic framework for learning when one relation implies another in knowledge bases, achieving higher AUC scores than prior methods and prov…
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya +6
The paper introduces a hierarchical probabilistic model that captures uncertainty in medical image segmentation by using latent variables at multiple resolutions, trained via a var…
Decision Tree Learning for Uncertain Clinical Measurements
CecÃlia Nunes, Hélène Langet, Mathieu De Craene +3
The paper introduces a probabilistic decision‑tree method that explicitly models measurement noise during split‑threshold selection, training instance splitting, and prediction, sh…