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Found 10 papers, 4 papers with code
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
In the rapidly evolving landscape of deep learning, the quest for models that balance expressivity with computational efficiency has never been more critical.
HiGen: Hierarchical Graph Generative Networks
Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods.
On Hierarchical Multi-Resolution Graph Generative Models
In real world domains, most graphs naturally exhibit a hierarchical structure.
DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders
Similar to other differentially private (DP) learners, the major challenge for DPGM is also how to achieve a subtle balance between utility and privacy.
Robust One Round Federated Learning with Predictive Space Bayesian Inference
In contrast, our method performs the aggregation on the predictive posteriors, which are typically easier to approximate owing to the low-dimensionality of the output space.
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
We present Federated Bayesian Neural Regression (FedBNR), an algorithm that learns a scalable stand-alone global federated GP that respects clients' privacy.
Variational Inference for Deep Probabilistic Canonical Correlation Analysis
In this paper, we propose a deep probabilistic multi-view model that is composed of a linear multi-view layer based on probabilistic canonical correlation analysis (CCA) description in the latent space together with deep generative networks as observation models.
Invertible Convolutional Flow
We show that these transforms allow more effective normalizing flow models to be developed for generative image models.
SignCol: Open-Source Software for Collecting Sign Language Gestures
Sign(ed) languages use gestures, such as hand or head movements, for communication.
Multi-view Matrix Factorization for Linear Dynamical System Estimation
In this paper, we instead reconsider likelihood maximization and develop an optimization based strategy for recovering the latent states and transition parameters.
