116 citations · 139 across the 15 of their papers we have counts for
15 papers
UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking
V. Bugra Yesilkaynak, Emine Dari, Alican Mertan +1
Existing multi-label ranking (MLR) frameworks only exploit information deduced from the bipartition of labels into positive and negative sets. Therefore, they do not benefit from r…
Morphological Cognition: Classifying MNIST Digits Through Morphological Computation Alone
Alican Mertan, Nick Cheney
With the rise of modern deep learning, neural networks have become an essential part of virtually every artificial intelligence system, making it difficult even to imagine differen…
Evolutionary Brain-Body Co-Optimization Consistently Fails to Select for Morphological Potential
Alican Mertan, Nick Cheney
Brain-body co-optimization remains a challenging problem. To understand and overcome its challenges, we exhaustively map a morphology-fitness landscape: we train controllers for ea…
Controller Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites
Alican Mertan, Nick Cheney
Brain-body co-optimization suffers from fragile co-adaptation where brains become over-specialized for particular bodies, hindering their ability to transfer well to others. Evolut…
No-brainer: Morphological Computation driven Adaptive Behavior in Soft Robots
Alican Mertan, Nick Cheney
It is prevalent in contemporary AI and robotics to separately postulate a brain modeled by neural networks and employ it to learn intelligent and adaptive behavior. While this meth…
Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation
Alican Mertan, Nick Cheney
Finding controllers that perform well across multiple morphologies is an important milestone for large-scale robotics, in line with recent advances via foundation models in other a…
Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots
Alican Mertan, Nick Cheney
Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials…
Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots
Alican Mertan, Nick Cheney
Soft robotics is a rapidly growing area of robotics research that would benefit greatly from design automation, given the challenges of manually engineering complex, compliant, and…
GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking
V. Bugra Yesilkaynak, Emine Dari, Alican Mertan +1
Existing multi-label frameworks only exploit the information deduced from the bipartition of the labels into a positive and negative set. Therefore, they do not benefit from the ra…
RLSEP: Learning Label Ranks for Multi-label Classification
Emine Dari, V. Bugra Yesilkaynak, Alican Mertan +1
Multi-label ranking maps instances to a ranked set of predicted labels from multiple possible classes. The ranking approach for multi-label learning problems received attention for…
Symmetry and Variance: Generative Parametric Modelling of Historical Brick Wall Patterns
Sevgi Altun, Mustafa Cem Gunes, Yusuf H. Sahin +3
This study integrates artificial intelligence and computational design tools to extract information from architectural heritage. Photogrammetry-based point cloud models of brick wa…
Single Image Depth Estimation: An Overview
Alican Mertan, Damien Jade Duff, Gozde Unal
We review solutions to the problem of depth estimation, arguably the most important subtask in scene understanding. We focus on the single image depth estimation problem. Due to it…
ODFNet: Using orientation distribution functions to characterize 3D point clouds
Yusuf H. Sahin, Alican Mertan, Gozde Unal
Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neu…
A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation
Alican Mertan, Yusuf Huseyin Sahin, Damien Jade Duff +1
We propose a new approach for the problem of relative depth estimation from a single image. Instead of directly regressing over depth scores, we formulate the problem as estimation…
Relative Depth Estimation as a Ranking Problem
Alican Mertan, Damien Jade Duff, Gozde Unal
We present a formulation of the relative depth estimation from a single image problem, as a ranking problem. By reformulating the problem this way, we were able to utilize literatu…