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Found 37 papers, 14 papers with code
Discrete Latent Graph Generative Modeling with Diffusion Bridges
Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre performance.
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence.
Mimicking Better by Matching the Approximate Action Distribution
In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations.
Discrete Graph Auto-Encoder
Despite advances in generative methods, accurately modeling the distribution of graphs remains a challenging task primarily because of the absence of predefined or inherent unique graph representation.
GrannGAN: Graph annotation generative adversarial networks
The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases.
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models
The combination of deep neural nets and theory-driven models, which we call deep grey-box modeling, can be inherently interpretable to some extent thanks to the theory backbone.
Permutation Equivariant Generative Adversarial Networks for Graphs
One solution consists of using equivariant generative functions, which ensure the ordering invariance.
Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning
Only our framework allowed us to design a method that performed well across the spectrum while remaining modular if more information about the quality of the data ever becomes available.
Conditional Neural Relational Inference for Interacting Systems
In this work, we want to learn to model the dynamics of similar yet distinct groups of interacting objects.
Learned transform compression with optimized entropy encoding
We consider the problem of learned transform compression where we learn both, the transform as well as the probability distribution over the discrete codes.
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
A key technical challenge is to strike a balance between the incomplete physics and trainable components such as neural networks for ensuring that the physics part is used in a meaningful manner.
Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory
Episodic and semantic memory are critical components of the human memory model.
ProxyFAUG: Proximity-based Fingerprint Augmentation
The best performing published positioning method on this dataset is improved by 40% in terms of median error and 6% in terms of mean error, with the use of the augmented dataset.
Analysing the Data-Driven Approach of Dynamically Estimating Positioning Accuracy
More specifically, with the use of a public LoRaWAN dataset, the current work analyses: the repartition of the available training set between the tasks of determining the location estimates and the DAE, the concept of selecting a subset of the most reliable estimates, and the impact that the spatial distribution of the data has to the accuracy of the DAE.
Goal-directed Generation of Discrete Structures with Conditional Generative Models
Unfortunately, maximum likelihood training of such models often fails with the samples from the generative model inadequately respecting the input properties.
Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning
Despite the recent success of reinforcement learning in various domains, these approaches remain, for the most part, deterringly sensitive to hyper-parameters and are often riddled with essential engineering feats allowing their success.
Improving VAE generations of multimodal data through data-dependent conditional priors
We propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), which learns to differentiate between the individual mixture components and therefore allows for generations from the distributional data clusters.
A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning
To facilitate the reproducibility of tests and comparability of results, the code and train/validation/test split used in this study are available.
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion
Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in data.
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN
The use of fingerprinting localization techniques in outdoor IoT settings has started to gain popularity over the recent years.
Learning by stochastic serializations
In this paper, we propose to map any complex structure onto a generic form, called serialization, over which we can apply any sequence-based density estimator.
Variational Saccading: Efficient Inference for Large Resolution Images
Image classification with deep neural networks is typically restricted to images of small dimensionality such as 224 x 244 in Resnet models [24].
Continual Classification Learning Using Generative Models
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences.
Sample-Efficient Imitation Learning via Generative Adversarial Nets
GAIL is a recent successful imitation learning architecture that exploits the adversarial training procedure introduced in GANs.
Structured nonlinear variable selection
We investigate structured sparsity methods for variable selection in regression problems where the target depends nonlinearly on the inputs.
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regression.
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks
We present a new method for forecasting systems of multiple interrelated time series.
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series.
Lifelong Generative Modeling
Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner.
Regularising Non-linear Models Using Feature Side-information
In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance.
Space-Time Local Embeddings
We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space.
Factorizing LambdaMART for cold start recommendations
Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees.
Learning Leading Indicators for Time Series Predictions
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system.
Two-Stage Metric Learning
This induces in the input data space a new family of distance metric with unique properties.
A Metric-learning based framework for Support Vector Machines and Multiple Kernel Learning
Recently, SVMs have been analyzed from SVM and metric learning, and to develop new algorithms that build on the strengths of each.
Parametric Local Metric Learning for Nearest Neighbor Classification
We present a new parametric local metric learning method in which we learn a smooth metric matrix function over the data manifold.
Metric Learning with Multiple Kernels
However, the problem then becomes finding the appropriate kernel function.
