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03/27/2023
Exploring Continual Learning of Diffusion Models
Diffusion models have achieved remarkable success in generating high-qua...
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02/20/2023
Analyzing the Posterior Collapse in Hierarchical Variational Autoencoders
Hierarchical Variational Autoencoders (VAEs) are among the most popular ...
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01/31/2023
Learning Data Representations with Joint Diffusion Models
We introduce a joint diffusion model that simultaneously learns meaningf...
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01/25/2023
Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN
Performant Convolutional Neural Network (CNN) architectures must be tail...
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12/23/2022
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
We study the problem of combining neural networks with symbolic reasonin...
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06/07/2022
Towards a General Purpose CNN for Long Range Dependencies in ND
The use of Convolutional Neural Networks (CNNs) is widespread in Deep Le...
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05/31/2022
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art pe...
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03/18/2022
Defending Variational Autoencoders from Adversarial Attacks with MCMC
Variational autoencoders (VAEs) are deep generative models used in vario...
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10/15/2021
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
When designing Convolutional Neural Networks (CNNs), one must select the...
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09/22/2021
Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization
Spiking neural networks are a promising approach towards next-generation...
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07/17/2021
The Effects of Learning in Morphologically Evolving Robot Systems
When controllers (brains) and morphologies (bodies) of robots simultaneo...
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04/01/2021
Storchastic: A Framework for General Stochastic Automatic Differentiation
Modelers use automatic differentiation of computation graphs to implemen...
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03/10/2021
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
In this work, we explore adversarial attacks on the Variational Autoenco...
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02/04/2021
Invertible DenseNets with Concatenated LipSwish
We introduce Invertible Dense Networks (i-DenseNets), a more parameter e...
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02/04/2021
CKConv: Continuous Kernel Convolution For Sequential Data
Conventional neural architectures for sequential data present important ...
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11/30/2020
General Invertible Transformations for Flow-based Generative Modeling
In this paper, we present a new class of invertible transformations. We ...
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10/19/2020
ABC-Di: Approximate Bayesian Computation for Discrete Data
Many real-life problems are represented as a black-box, i.e., the intern...
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10/19/2020
Learning Locomotion Skills in Evolvable Robots
The challenge of robotic reproduction – making of new robots by recombin...
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10/05/2020
i-DenseNets
We introduce Invertible Dense Networks (i-DenseNets), a more parameter e...
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10/05/2020
Self-Supervised Variational Auto-Encoders
Density estimation, compression and data generation are crucial tasks in...
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09/19/2020
Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae
Models in systems biology are mathematical descriptions of biological pr...
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06/09/2020
Wavelet Networks: Scale Equivariant Learning From Raw Waveforms
Inducing symmetry equivariance in deep neural architectures has resolved...
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06/09/2020
Super-resolution Variational Auto-Encoders
The framework of variational autoencoders (VAEs) provides a principled m...
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06/02/2020
The Convolution Exponential and Generalized Sylvester Flows
This paper introduces a new method to build linear flows, by taking the ...
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05/04/2020
Designing Data Augmentation for Simulating Interventions
Machine learning models trained with purely observational data and the p...
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05/04/2020
Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm
Not all generate-and-test search algorithms are created equal. Bayesian ...
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02/07/2020
Differential Evolution with Reversible Linear Transformations
Differential evolution (DE) is a well-known type of evolutionary algorit...
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02/07/2020
Attentive Group Equivariant Convolutional Networks
Although group convolutional networks are able to learn powerful represe...
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01/30/2020
Learning Discrete Distributions by Dequantization
Media is generally stored digitally and is therefore discrete. Many succ...
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01/21/2020
Learning Directed Locomotion in Modular Robots with Evolvable Morphologies
We generalize the well-studied problem of gait learning in modular robot...
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10/07/2019
Increasing Expressivity of a Hyperspherical VAE
Learning suitable latent representations for observed, high-dimensional ...
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08/14/2019
Video Compression With Rate-Distortion Autoencoders
In this paper we present a a deep generative model for lossy video compr...
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05/24/2019
DIVA: Domain Invariant Variational Autoencoders
We consider the problem of domain generalization, namely, how to learn r...
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04/26/2019
Simulating Execution Time of Tensor Programs using Graph Neural Networks
Optimizing the execution time of tensor program, e.g., a convolution, in...
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02/01/2019
Combinatorial Bayesian Optimization using Graph Representations
This paper focuses on Bayesian Optimization - typically considered with ...
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06/26/2018
Hierarchical VampPrior Variational Fair Auto-Encoder
Decision making is a process that is extremely prone to different biases...
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04/03/2018
Hyperspherical Variational Auto-Encoders
The Variational Auto-Encoder (VAE) is one of the most used unsupervised ...
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03/15/2018
Sylvester Normalizing Flows for Variational Inference
Variational inference relies on flexible approximate posterior distribut...
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02/13/2018
Attention-based Deep Multiple Instance Learning
Multiple instance learning (MIL) is a variation of supervised learning w...
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12/01/2017
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification
The computer-aided analysis of medical scans is a longstanding goal in t...
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06/07/2017
Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow
In this paper, we propose a new volume-preserving flow and show that it ...
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05/19/2017
VAE with a VampPrior
Many different methods to train deep generative models have been introdu...
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10/23/2016
