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08/21/2023
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs
Real-world graphs are dynamic, constantly evolving with new interactions...
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08/13/2023
Probabilistic Imputation for Time-series Classification with Missing Data
Multivariate time series data for real-world applications typically cont...
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07/12/2023
Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models
Large-scale image generation models, with impressive quality made possib...
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06/20/2023
Traversing Between Modes in Function Space for Fast Ensembling
Deep ensemble is a simple yet powerful way to improve the performance of...
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06/01/2023
Regularizing Towards Soft Equivariance Under Mixed Symmetries
Datasets often have their intrinsic symmetries, and particular deep-lear...
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05/24/2023
SWAMP: Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
Given the ever-increasing size of modern neural networks, the significan...
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04/19/2023
Martingale Posterior Neural Processes
A Neural Process (NP) estimates a stochastic process implicitly defined ...
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04/19/2023
Decoupled Training for Long-Tailed Classification With Stochastic Representations
Decoupling representation learning and classifier learning has been show...
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04/11/2023
Bayesian Analysis of Generalized Hierarchical Indian Buffet Processes for Within and Across Group Sharing of Latent Features
Bayesian nonparametric hierarchical priors provide flexible models for s...
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03/13/2023
5G-Advanced Towards 6G: Past, Present, and Future
Since the start of 5G work in 3GPP in early 2016, tremendous progress ha...
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02/02/2023
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
We consider the optimisation of large and shallow neural networks via gr...
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10/12/2022
On Divergence Measures for Bayesian Pseudocoresets
A Bayesian pseudocoreset is a small synthetic dataset for which the post...
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10/05/2022
Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders
Masked image modeling (MIM) has become a popular strategy for self-super...
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08/26/2022
Universal Mini-Batch Consistency for Set Encoding Functions
Previous works have established solid foundations for neural set functio...
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06/30/2022
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation
Ensembles of deep neural networks have demonstrated superior performance...
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05/20/2022
Set-based Meta-Interpolation for Few-Task Meta-Learning
Meta-learning approaches enable machine learning systems to adapt to new...
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05/17/2022
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
This article studies the infinite-width limit of deep feedforward neural...
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02/02/2022
PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems
In multiple-input multiple-output (MIMO) systems, the high-resolution ch...
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10/27/2021
Diversity Matters When Learning From Ensembles
Deep ensembles excel in large-scale image classification tasks both in t...
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10/12/2021
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty
Numerous recent works utilize bi-Lipschitz regularization of neural netw...
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10/06/2021
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
Multilingual models jointly pretrained on multiple languages have achiev...
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07/03/2021
Scale Mixtures of Neural Network Gaussian Processes
Recent works have revealed that infinitely-wide feed-forward or recurren...
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06/11/2021
Learning to Pool in Graph Neural Networks for Extrapolation
Graph neural networks (GNNs) are one of the most popular approaches to u...
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06/11/2021
Adversarial purification with Score-based generative models
While adversarial training is considered as a standard defense method ag...
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06/11/2021
Hybrid Generative-Contrastive Representation Learning
Unsupervised representation learning has recently received lots of inter...
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05/06/2021
Learning to Perturb Word Embeddings for Out-of-distribution QA
QA models based on pretrained language mod-els have achieved remarkable ...
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03/29/2021
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Generative modeling of set-structured data, such as point clouds, requir...
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03/21/2021
Posterior distributions for Hierarchical Spike and Slab Indian Buffet processes
Bayesian nonparametric hierarchical priors are highly effective in provi...
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03/02/2021
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding
Most existing set encoding algorithms operate under the assumption that ...
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02/22/2021
Improving Uncertainty Calibration via Prior Augmented Data
Neural networks have proven successful at learning from complex data dis...
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10/29/2020
Attentive Clustering Processes
Amortized approaches to clustering have recently received renewed attent...
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08/07/2020
Bootstrapping Neural Processes
Unlike in the traditional statistical modeling for which a user typicall...
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08/07/2020
Neural Complexity Measures
While various complexity measures for diverse model classes have been pr...
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06/25/2020
Stochastic Subset Selection
Current machine learning algorithms are designed to work with huge volum...
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06/19/2020
The Normal-Generalised Gamma-Pareto process: A novel pure-jump Lévy process with flexible tail and jump-activity properties
Pure-jump Lévy processes are popular classes of stochastic processes whi...
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06/09/2020
Cost-effective Interactive Attention Learning with Neural Attention Processes
We propose a novel interactive learning framework which we refer to as I...
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10/17/2019
Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
Generative models of graph structure have applications in biology and so...
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09/30/2019
Deep Amortized Clustering
We propose a deep amortized clustering (DAC), a neural architecture whic...
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05/26/2019
A unified construction for series representations and finite approximations of completely random measures
Infinite-activity completely random measures (CRMs) have become importan...
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02/13/2019
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior
Bayesian nonparametric approaches, in particular the Pitman-Yor process ...
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10/03/2018
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
We consider a non-projective class of inhomogeneous random graph models ...
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10/01/2018
Set Transformer
Many machine learning tasks such as multiple instance learning, 3D shape...
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06/05/2018
Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare
We present a personalized and reliable prediction model for healthcare, ...
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05/28/2018
Adaptive Network Sparsification via Dependent Variational Beta-Bernoulli Dropout
While variational dropout approaches have been shown to be effective for...
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05/25/2018
Transductive Propagation Network for Few-shot Learning
Few-shot learning aims to build a learner that quickly generalizes to no...
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05/24/2018
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Attention mechanism is effective in both focusing the deep learning mode...
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12/21/2017
DropMax: Adaptive Stochastic Softmax
We propose DropMax, a stochastic version of softmax classifier which at ...
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02/27/2017
Bayesian inference on random simple graphs with power law degree distributions
We present a model for random simple graphs with a degree distribution t...
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11/18/2015
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models
Normalized random measures (NRMs) provide a broad class of discrete rand...
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01/29/2015
