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06/16/2023
A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning
We propose a novel hierarchical Bayesian model for learning with a large...
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05/08/2023
FedHB: Hierarchical Bayesian Federated Learning
We propose a novel hierarchical Bayesian approach to Federated Learning ...
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02/23/2023
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
We tackle the domain generalisation (DG) problem by posing it as a domai...
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06/10/2022
Fisher SAM: Information Geometry and Sharpness Aware Minimisation
Recent sharpness-aware minimisation (SAM) is known to find flat minima w...
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04/15/2022
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
Few-shot learning (FSL) is an important and topical problem in computer ...
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03/29/2022
DesCert: Design for Certification
The goal of the DARPA Automated Rapid Certification Of Software (ARCOS) ...
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11/10/2021
SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval
We tackle the cross-modal retrieval problem, where the training is only ...
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11/09/2021
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation
The meta learning few-shot classification is an emerging problem in mach...
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07/19/2021
Synthesizing Human Faces using Latent Space Factorization and Local Weights (Extended Version)
We propose a 3D face generative model with local weights to increase the...
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02/10/2021
On PyTorch Implementation of Density Estimators for von Mises-Fisher and Its Mixture
The von Mises-Fisher (vMF) is a well-known density model for directional...
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02/05/2021
Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach
Variational autoencoder (VAE) is a very successful generative model whos...
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12/01/2020
Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach
We deal with the problem of learning the underlying disentangled latent ...
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11/17/2020
Recursive Inference for Variational Autoencoders
Inference networks of traditional Variational Autoencoders (VAEs) are ty...
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09/07/2020
Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models
In deep representational learning, it is often desired to isolate a part...
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09/26/2019
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
Domain Adaptation (DA), the process of effectively adapting task models ...
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09/06/2019
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement
We propose a family of novel hierarchical Bayesian deep auto-encoder mod...
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07/25/2019
Probabilistic Approximate Logic and its Implementation in the Logical Imagination Engine
In spite of the rapidly increasing number of applications of machine lea...
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05/16/2019
Efficient Deep Gaussian Process Models for Variable-Sized Input
Deep Gaussian processes (DGP) have appealing Bayesian properties, can ha...
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02/23/2019
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
In unsupervised domain adaptation, it is widely known that the target do...
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02/05/2019
Relevance Factor VAE: Learning and Identifying Disentangled Factors
We propose a novel VAE-based deep auto-encoder model that can learn dise...
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02/02/2019
Online Multi-Object Tracking with Dual Matching Attention Networks
In this paper, we propose an online Multi-Object Tracking (MOT) approach...
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09/28/2016
Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking
Multi-object tracking has recently become an important area of computer ...
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03/06/2015