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Found 17 papers, 4 papers with code
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation
Multi-modality magnetic resonance imaging data with various sequences facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC).
Guided Decoding for Robot Motion Generation and Adaption
This integration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajectories.
DomainLab: A modular Python package for domain generalization in deep learning
DomainLab is a modular Python package for training user specified neural networks with composable regularization loss terms.
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
We address the online combinatorial choice of weight multipliers for multi-objective optimization of many loss terms parameterized by neural works via a probabilistic graphical model (PGM) for the joint model parameter and multiplier evolution process, with a hypervolume based likelihood promoting multi-objective descent.
Sequential Model for Predicting Patient Adherence in Subcutaneous Immunotherapy for Allergic Rhinitis
Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in Allergic Rhinitis (AR) patients.
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography
Supernova spectral time series can be used to reconstruct a spatially resolved explosion model known as supernova tomography.
Local Distance Preserving Auto-encoders using Continuous k-Nearest Neighbours Graphs
Auto-encoder models that preserve similarities in the data are a popular tool in representation learning.
Flat Latent Manifolds for Human-machine Co-creation of Music
We therefore consider a music-generating algorithm as a counterpart to a human musician, in a setting where reciprocal interplay is to lead to new experiences, both for the musician and the audience.
Learning Flat Latent Manifolds with VAEs
Prevalent is the use of the Euclidean metric, which has the drawback of ignoring information about similarity of data stored in the decoder, as captured by the framework of Riemannian geometry.
FLAT MANIFOLD VAES
Latent-variable models represent observed data by mapping a prior distribution over some latent space to an observed space.
Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images
In addition, compared with previous single finger estimation in an experimental environment, we extend the approach to multiple finger force estimation, which can be used for applications such as human grasping analysis.
Increasing the Generalisation Capacity of Conditional VAEs
We address the problem of one-to-many mappings in supervised learning, where a single instance has many different solutions of possibly equal cost.
Learning Hierarchical Priors in VAEs
We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution.
Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity.
Active Learning based on Data Uncertainty and Model Sensitivity
It relies on the Jacobian of the likelihood to detect non-smooth transitions in the latent space, i. e., transitions that lead to abrupt changes in the movement of the robot.
Metrics for Deep Generative Models
Neural samplers such as variational autoencoders (VAEs) or generative adversarial networks (GANs) approximate distributions by transforming samples from a simple random source---the latent space---to samples from a more complex distribution represented by a dataset.
On Fast Dropout and its Applicability to Recurrent Networks
Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data.
