Description
Book Synopsis.- Clustering and Segmentation.
.- PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks.
.- A State-of-the-Art Cutting Plane Algorithm for Clique Partitioning.
.- Self-Supervised Semantic Segmentation from Audio-Visual Data.
.- BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation.
.- Learning Techniques.
.- FullCert: Deterministic End-to-End Certification for Training and Inference of Neural Networks.
.- Self-Masking Networks for Unsupervised Adaptation.
.- A Theoretical Formulation on the Use of Multiple Positive Views in Contrastive Learning
.- Decoupling of neural network calibration measures.
.- Examining Common Paradigms in Multi-Task Learning.
.- DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning.
.- Efficient and Discriminative Image Feature Extraction for Universal Image Retrieval ..
.- Anomaly Detection with Conditioned Denoising Diffusion Models.
.- Medical and Biological Applications.
.- SurgeoNet: Realtime 3D Pose Estimation of Articulated Surgical Instruments from Stereo Images using a Synthetically-trained Network.
.- Foundation Models Permit Retinal Layer Segmentation Across OCT Devices.
.- Correlation Clustering of Organoid Images.
.- Animal Identification with Independent Foreground and Background Modeling.
.- Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks.
.- Bigger Isn’t Always Better: Towards a General Prior for Medical Image Reconstruction.
.- Uncertainty and Explainability.
.- Latent Diffusion Counterfactual Explanations.
.- Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations.
.- Uncertainty Voting Ensemble for Imbalanced Deep Regression.
.- Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning.
