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06/16/2023
Sample-Efficient On-Policy Imitation Learning from Observations
Imitation learning from demonstrations (ILD) aims to alleviate numerous ...
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06/13/2023
Vector-Quantized Graph Auto-Encoder
In this work, we addresses the problem of modeling distributions of grap...
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12/01/2022
GrannGAN: Graph annotation generative adversarial networks
We consider the problem of modelling high-dimensional distributions and ...
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10/24/2022
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 c...
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12/07/2021
Permutation Equivariant Generative Adversarial Networks for Graphs
One of the most discussed issues in graph generative modeling is the ord...
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07/03/2021
Where is the Grass Greener? Revisiting Generalized Policy Iteration for Offline Reinforcement Learning
The performance of state-of-the-art baselines in the offline RL regime v...
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06/21/2021
Conditional Neural Relational Inference for Interacting Systems
In this work, we want to learn to model the dynamics of similar yet dist...
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04/07/2021
Learned transform compression with optimized entropy encoding
We consider the problem of learned transform compression where we learn ...
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02/25/2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
Integrating physics models within machine learning holds considerable pr...
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02/20/2021
Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory
Episodic and semantic memory are critical components of the human memory...
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02/04/2021
ProxyFAUG: Proximity-based Fingerprint Augmentation
The proliferation of data-demanding machine learning methods has brought...
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11/20/2020
Analysing the Data-Driven Approach of Dynamically Estimating Positioning Accuracy
The primary expectation from positioning systems is for them to provide ...
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10/05/2020
Goal-directed Generation of Discrete Structures with Conditional Generative Models
Despite recent advances, goal-directed generation of structured discrete...
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06/28/2020
Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning
Despite the recent success of reinforcement learning in various domains,...
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11/25/2019
Improving VAE generations of multimodal data through data-dependent conditional priors
One of the major shortcomings of variational autoencoders is the inabili...
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08/14/2019
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN
The use of fingerprinting localization techniques in outdoor IoT setting...
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08/14/2019
A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning
Fingerprinting techniques, which are a common method for indoor localiza...
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08/14/2019
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion
Learning embeddings of entities and relations existing in knowledge base...
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05/27/2019
Learning by stochastic serializations
Complex structures are typical in machine learning. Tailoring learning a...
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12/08/2018
Variational Saccading: Efficient Inference for Large Resolution Images
Image classification with deep neural networks is typically restricted t...
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10/24/2018
Continual Classification Learning Using Generative Models
Continual learning is the ability to sequentially learn over time by acc...
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09/06/2018
Sample-Efficient Imitation Learning via Generative Adversarial Nets
Recent work in imitation learning articulate their formulation around th...
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05/16/2018
Structured nonlinear variable selection
We investigate structured sparsity methods for variable selection in reg...
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04/19/2018
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regres...
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10/02/2017
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...
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06/27/2017
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Traditional linear methods for forecasting multivariate time series are ...
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05/27/2017
Lifelong Generative Modeling
Lifelong learning is the problem of learning multiple consecutive tasks ...
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03/07/2017
Regularising Non-linear Models Using Feature Side-information
Very often features come with their own vectorial descriptions which pro...
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07/07/2015
Learning Leading Indicators for Time Series Predictions
We consider the problem of learning models for forecasting multiple time...
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05/12/2014
Two-Stage Metric Learning
In this paper, we present a novel two-stage metric learning algorithm. W...
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10/04/2012
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining
The notion of meta-mining has appeared recently and extends the traditio...
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01/23/2012
