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12 accepted papers

2026

Knowing When to Quit: Probabilistic Early Exits for Speech Separation Networks

ICLR 2026poster

In recent years, deep learning-based single-channel speech separation has improved considerably, in large part driven by increasingly compute- and parameter-efficient neural network architectures. Most such architectures are, however, designed with a fixed compute and parameter budget, and consequen…

Cited by 0SourceScholar
2025

How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings

ICLR 2025poster

Low-dimensional embeddings are essential for machine learning tasks involving graphs, such as node classification, link prediction, community detection, network visualization, and network compression. Although recent studies have identified exact low-dimensional embeddings, the limits of the require…

2025

SepMamba: State-Space Models for Speaker Separation Using Mamba

ICASSP 2025accepted

Deep learning-based single-channel speaker separation has improved significantly in recent years in large part due to the introduction of the transformer-based attention mechanism. However, these improvements come with intense computational demands, precluding their use in many practical application…

Cited by 0SourceScholar
2024

Continuous-Time Graph Representation with Sequential Survival Process

AAAI 2024technical

Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, including bioinformatics, chemistry, and the social sciences. However, current dynamic network approaches focus on discrete-t…

Cited by 3SourcePDFScholar
2024

Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators

AISTATS 2024poster

Kernel density estimation (KDE) is a powerful technique for non-parametric density estimation, yet practical use of KDE-based methods remains limited by insufficient representational flexibility, especially for higher-dimensional data. Contrary to KDE, K-nearest neighbor (KNN) density estimation pro…

2024

Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model

AISTATS 2024poster

Understanding the structure and dynamics of scientific research, i.e., the science of science (SciSci), has become an important area of research in order to address imminent questions including how scholars interact to advance science, how disciplines are related and evolve, and how research impact…

Cited by 0SourcePDFScholar
2023

CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion

NeurIPS 2023poster

Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique providing insights into brain function. Unfortunately, EEG data exhibit a high degree of noise and variability across subjects hampering generalizable signal extraction. Therefore, a key aim in EEG analysis is to extract…

2023

Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model

AISTATS 2023poster

Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typically, these representation learning approaches embed the networks into a low-dimensional space in which the role of eac…

2018

Evaluating Models of Dynamic Functional Connectivity Using Predictive Classification Accuracy

ICASSP 2018accepted

Dynamic functional connectivity has become a prominent approach for tracking the changes of macroscale statistical dependencies between regions in the brain. Effective parametrization of these statistical dependencies, referred to as brain states, is however still an open problem. We investigate dif…

Cited by 0SourceScholar
2017

Scalable group level probabilistic sparse factor analysis

ICASSP 2017accepted

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component…

Cited by 0SourceScholar
2016

Completely random measures for modelling block-structured sparse networks

NeurIPS 2016poster

Statistical methods for network data often parameterize the edge-probability by attributing latent traits such as block structure to the vertices and assume exchangeability in the sense of the Aldous-Hoover representation theorem. These assumptions are however incompatible with traits found in real-…

Cited by 56SourcePDFScholar