Search Results for author: Martin Takac

Found 9 papers, 3 papers with code

Learning to generalize Dispatching rules on the Job Shop Scheduling

1 code implementation9 Jun 2022 Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takac

Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem.

Learning to Control under Time-Varying Environment

no code implementations6 Jun 2022 Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takac, Bin Gu

To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.

Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input

no code implementations10 Mar 2022 Guangyi Liu, Arash Amini, Martin Takac, Nader Motee

For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.

Classification

Distributed Learning With Sparsified Gradient Differences

no code implementations5 Feb 2022 Yicheng Chen, Rick S. Blum, Martin Takac, Brian M. Sadler

A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.

Improving Text-to-Image Synthesis Using Contrastive Learning

1 code implementation6 Jul 2021 Hui Ye, Xiulong Yang, Martin Takac, Rajshekhar Sunderraman, Shihao Ji

To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images.

Contrastive Learning Text-to-Image Generation

On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches

no code implementations14 Jul 2018 Jie Liu, Yu Rong, Martin Takac, Junzhou Huang

This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.

Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption

no code implementations16 Dec 2016 Jie Liu, Martin Takac

We propose a projected semi-stochastic gradient descent method with mini-batch for improving both the theoretical complexity and practical performance of the general stochastic gradient descent method (SGD).

Machine Learning

Matrix Completion under Interval Uncertainty

no code implementations11 Aug 2014 Jakub Marecek, Peter Richtarik, Martin Takac

Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints.

Collaborative Filtering Matrix Completion

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