activity
20202026
most citedADRMX: Additive Disentanglement of Domain Features with Remix Loss

3 citations · 3 across the 7 of their papers we have counts for

collaborators

8 papers

cs.LG2026

-JEPA Spectral Anti-Collapse Regularization for Self-Supervised Learning

Berker Demirel, Clémentine Dominé, Valentino Maiorca +3

Joint-embedding self-supervised learning typically combines an invariance objective across augmented views with additional mechanisms to prevent representational collapse. These ob…

cs.LG2026

Assessing Sample Quality in Conditional Generation under Compositional Shift

Berker Demirel, Valentino Maiorca, Marco Fumero +2

Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimenta…

q-bio.QM2025

MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging

Berker Demirel, Marco Fumero, Theofanis Karaletsos +1

Simulating in silico cellular responses to interventions is a promising direction to accelerate high-content image-based assays, critical for advancing drug discovery and gene edit…

cs.LG2024

Adjusting Pretrained Backbones for Performativity

Berker Demirel, Lingjing Kong, Kun Zhang +3

With the widespread deployment of deep learning models, they influence their environment in various ways. The induced distribution shifts can lead to unexpected performance degrada…

cs.LG2024

Out-of-Distribution Detection with Relative Angles

Berker Demirel, Marco Fumero, Francesco Locatello

Deep learning systems deployed in real-world applications often encounter data that is different from their in-distribution (ID). A reliable model should ideally abstain from makin…

cs.LG2023★ 3 cited

ADRMX: Additive Disentanglement of Domain Features with Remix Loss

Berker Demirel, Erchan Aptoula, Huseyin Ozkan

The common assumption that train and test sets follow similar distributions is often violated in deployment settings. Given multiple source domains, domain generalization aims to c…

cs.CV2023

DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image

Berker Demirel, Huseyin Ozkan

Group Activity Recognition (GAR) aims to detect the activity performed by multiple actors in a scene. Prior works model the spatio-temporal features based on the RGB, optical flow…

cs.DS2020

Vertex Ordering Algorithms for Graph Coloring Problem

Arda Asik, Ibrahim Bugra Demir, Berker Demirel +2

Graph coloring is a fundamental problem in combinatorics with many applications in practice. In this problem, the vertices in a given graph must be colored by using the least numbe…