Tolga Ergen

Orcid: 0000-0003-4806-0224

According to our database1, Tolga Ergen authored at least 35 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
The Convex Landscape of Neural Networks: Characterizing Global Optima and Stationary Points via Lasso Models.
CoRR, 2023

Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Parallel Deep Neural Networks Have Zero Duality Gap.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Globally Optimal Training of Neural Networks with Threshold Activation Functions.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Convexifying Transformers: Improving optimization and understanding of transformer networks.
CoRR, 2022

GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction.
CoRR, 2022

Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers.
Proceedings of the International Conference on Machine Learning, 2022

Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Convex Geometry and Duality of Over-parameterized Neural Networks.
J. Mach. Learn. Res., 2021

Revealing the Structure of Deep Neural Networks via Convex Duality.
Proceedings of the 38th International Conference on Machine Learning, 2021

Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs.
Proceedings of the 38th International Conference on Machine Learning, 2021

Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms.
Proceedings of the 9th International Conference on Learning Representations, 2021

Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time.
Proceedings of the 9th International Conference on Learning Representations, 2021

Convex Neural Autoregressive Models: Towards Tractable, Expressive, and Theoretically-Backed Models for Sequential Forecasting and Generation.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Energy-Efficient LSTM Networks for Online Learning.
IEEE Trans. Neural Networks Learn. Syst., 2020

Unsupervised Anomaly Detection With LSTM Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., 2020

A novel distributed anomaly detection algorithm based on support vector machines.
Digit. Signal Process., 2020

Training Convolutional ReLU Neural Networks in Polynomial Time: Exact Convex Optimization Formulations.
CoRR, 2020

Convex Duality of Deep Neural Networks.
CoRR, 2020

Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020

Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Team-optimal online estimation of dynamic parameters over distributed tree networks.
Signal Process., 2019

Convex Optimization for Shallow Neural Networks.
Proceedings of the 57th Annual Allerton Conference on Communication, 2019

2018
Online Training of LSTM Networks in Distributed Systems for Variable Length Data Sequences.
IEEE Trans. Neural Networks Learn. Syst., 2018

Efficient Online Learning Algorithms Based on LSTM Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., 2018

Recurrent neural networks based online learning algorithms for distributed systems.
Proceedings of the 26th Signal Processing and Communications Applications Conference, 2018

A novel anomaly detection approach based on neural networks.
Proceedings of the 26th Signal Processing and Communications Applications Conference, 2018

A highly efficient recurrent neural network architecture for data regression.
Proceedings of the 26th Signal Processing and Communications Applications Conference, 2018

2017
Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks.
CoRR, 2017

An efficient bandit algorithm for general weight assignments.
Proceedings of the 25th Signal Processing and Communications Applications Conference, 2017

Online distributed nonlinear regression via neural networks.
Proceedings of the 25th Signal Processing and Communications Applications Conference, 2017

Neural networks based online learning.
Proceedings of the 25th Signal Processing and Communications Applications Conference, 2017

Novelty detection using soft partitioning and hierarchical models.
Proceedings of the 25th Signal Processing and Communications Applications Conference, 2017


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