4 citations · 10 across the 13 of their papers we have counts for
5 papers · 1 filter
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Konstantin Schürholt, Dimche Kostadinov, Damian Borth
Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations. Neural Networks (NNs) are widely applied, yet their weight space is still…
Network Parameter Learning Using Nonlinear Transforms, Local Representation Goals and Local Propagation Constraints
Dimche Kostadinov, Behrooz Razdehi, Slava Voloshynovskiy
In this paper, we introduce a novel concept for learning of the parameters in a neural network. Our idea is grounded on modeling a learning problem that addresses a trade-off betwe…
Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment
Dimche Kostadinov, Behrooz Razeghi, Taras Holotyak +1
This paper presents a novel clustering concept that is based on jointly learned nonlinear transforms (NTs) with priors on the information loss and the discrimination. We introduce…
Network Learning with Local Propagation
Dimche Kostadinov, Behrooz Razeghi, Sohrab Ferdowsi +1
This paper presents a locally decoupled network parameter learning with local propagation. Three elements are taken into account: (i) sets of nonlinear transforms that describe the…
Regularized Residual Quantization: a multi-layer sparse dictionary learning approach
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov
The Residual Quantization (RQ) framework is revisited where the quantization distortion is being successively reduced in multi-layers. Inspired by the reverse-water-filling paradig…