KTH Royal Institute of Technology

∙ Stockholm , Sweden ∙ 0 followers

Since its inception in 1827, KTH has developed into one of Europe's leading technical universities and an important arena for knowledge development. As Sweden's largest university for technical research and education, we bring students, researchers and faculty...

Shearlets as Feature Extractor for Semantic Edge Detection: The Model-Based and Data-Driven Realm

Semantic edge detection has recently gained a lot of attention as an ima...
11/27/2019 ∙ by Héctor Andrade-Loarca, et al. ∙ 31 ∙ share

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Kernel of CycleGAN as a Principle homogeneous space

Unpaired image-to-image translation has attracted significant interest d...
01/24/2020 ∙ by Nikita Moriakov, et al. ∙ 25 ∙ share

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Deep Bayesian Inversion

Characterizing statistical properties of solutions of inverse problems i...
11/14/2018 ∙ by Jonas Adler, et al. ∙ 24 ∙ share

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Human-centered collaborative robots with deep reinforcement learning

We present a reinforcement learning based framework for human-centered c...
07/02/2020 ∙ by Ali Ghadirzadeh, et al. ∙ 23 ∙ share

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Which way? Direction-Aware Attributed Graph Embedding

Graph embedding algorithms are used to efficiently represent (encode) a ...
01/30/2020 ∙ by Zekarias T. Kefato, et al. ∙ 18 ∙ share

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Imitating by generating: deep generative models for imitation of interactive tasks

To coordinate actions with an interaction partner requires a constant ex...
10/14/2019 ∙ by Judith Butepage, et al. ∙ 17 ∙ share

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Neural Outlier Rejection for Self-Supervised Keypoint Learning

Identifying salient points in images is a crucial component for visual o...
12/23/2019 ∙ by Jiexiong Tang, et al. ∙ 15 ∙ share

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Finite-time Identification of Stable Linear Systems: Optimality of the Least-Squares Estimator

We provide a new finite-time analysis of the estimation error of stable ...
03/17/2020 ∙ by Yassir Jedra, et al. ∙ 15 ∙ share

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Coordinates-based Resource Allocation Through Supervised Machine Learning

Appropriate allocation of system resources is essential for meeting the ...
05/13/2020 ∙ by Sahar Imtiaz, et al. ∙ 14 ∙ share

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GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

In this paper, we present a deep learning-based network, GCNv2, for gene...
02/28/2019 ∙ by Jiexiong Tang, et al. ∙ 12 ∙ share

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Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

Enabling cellular connectivity for drones introduces a wide set of chall...
01/22/2020 ∙ by Amin Azari, et al. ∙ 11 ∙ share

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Hyperplane Arrangements of Trained ConvNets Are Biased

We investigate the geometric properties of the functions learned by trai...
03/17/2020 ∙ by Matteo Gamba, et al. ∙ 10 ∙ share

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α Belief Propagation for Approximate Inference

Belief propagation (BP) algorithm is a widely used message-passing metho...
06/27/2020 ∙ by Dong Liu, et al. ∙ 9 ∙ share

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Beyond the Self: Using Grounded Affordances to Interpret and Describe Others' Actions

We propose a developmental approach that allows a robot to interpret and...
02/26/2019 ∙ by Giovanni Saponaro, et al. ∙ 8 ∙ share

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Sparse2Dense: From direct sparse odometry to dense 3D reconstruction

In this paper, we proposed a new deep learning based dense monocular SLA...
03/21/2019 ∙ by Jiexiong Tang, et al. ∙ 8 ∙ share

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A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision

The 3D depth estimation and relative pose estimation problem within a de...
08/01/2019 ∙ by Romulo T. Rodrigues, et al. ∙ 8 ∙ share

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Neural Network based Explicit Mixture Models and Expectation-maximization based Learning

We propose two neural network based mixture models in this article. The ...
07/31/2019 ∙ by Dong Liu, et al. ∙ 5 ∙ share

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Entropy-regularized Optimal Transport Generative Models

We investigate the use of entropy-regularized optimal transport (EOT) co...
11/16/2018 ∙ by Dong Liu, et al. ∙ 4 ∙ share

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A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels

Image classification models built into visual support systems and other ...
01/03/2019 ∙ by Marcus Klasson, et al. ∙ 4 ∙ share

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Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks

Visual relationship detection is fundamental for holistic image understa...
06/16/2020 ∙ by Federico Baldassarre, et al. ∙ 4 ∙ share

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Incremental inference of collective graphical models

We consider incremental inference problems from aggregate data for colle...
06/26/2020 ∙ by Rahul Singh, et al. ∙ 4 ∙ share

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α Belief Propagation as Fully Factorized Approximation

Belief propagation (BP) can do exact inference in loop-free graphs, but ...
08/23/2019 ∙ by Dong Liu, et al. ∙ 3 ∙ share

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Bayesian nonparametric shared multi-sequence time series segmentation

In this paper, we introduce a method for segmenting time series data usi...
01/27/2020 ∙ by Olga Mikheeva, et al. ∙ 3 ∙ share

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Banach Wasserstein GAN

Wasserstein Generative Adversarial Networks (WGANs) can be used to gener...
06/18/2018 ∙ by Jonas Adler, et al. ∙ 2 ∙ share

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Optimal Attacks on Reinforcement Learning Policies

Control policies, trained using the Deep Reinforcement Learning, have be...
07/31/2019 ∙ by Alessio Russo, et al. ∙ 2 ∙ share

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Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks

This paper considers the distributed sampled-data control problem of a g...
09/07/2016 ∙ by Zhixin Liu, et al. ∙ 0 ∙ share

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Interactive Robot Learning of Gestures, Language and Affordances

A growing field in robotics and Artificial Intelligence (AI) research is...
11/24/2017 ∙ by Giovanni Saponaro, et al. ∙ 0 ∙ share

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Self-Supervised Vision-Based Detection of the Active Speaker as a Prerequisite for Socially-Aware Language Acquisition

This paper presents a self-supervised method for detecting the active sp...
11/24/2017 ∙ by Kalin Stefanov, et al. ∙ 0 ∙ share

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Minimal Exploration in Structured Stochastic Bandits

This paper introduces and addresses a wide class of stochastic bandit pr...
11/01/2017 ∙ by Richard Combes, et al. ∙ 0 ∙ share

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Advances in Variational Inference

Many modern unsupervised or semi-supervised machine learning algorithms ...
11/15/2017 ∙ by Cheng Zhang, et al. ∙ 0 ∙ share

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Learned Primal-dual Reconstruction

We propose the Learned Primal-Dual algorithm for tomographic reconstruct...
07/20/2017 ∙ by Jonas Adler, et al. ∙ 0 ∙ share

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Machine Learning and Social Robotics for Detecting Early Signs of Dementia

This paper presents the EACare project, an ambitious multi-disciplinary ...
09/05/2017 ∙ by Patrik Jonell, et al. ∙ 0 ∙ share

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On the Performance of Network Parallel Training in Artificial Neural Networks

Artificial Neural Networks (ANNs) have received increasing attention in ...
01/18/2017 ∙ by Ludvig Ericson, et al. ∙ 0 ∙ share

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Learning to solve inverse problems using Wasserstein loss

We propose using the Wasserstein loss for training in inverse problems. ...
10/30/2017 ∙ by Jonas Adler, et al. ∙ 0 ∙ share

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Unsupervised Object Discovery and Segmentation of RGBD-images

In this paper we introduce a system for unsupervised object discovery an...
10/18/2017 ∙ by Johan Ekekrantz, et al. ∙ 0 ∙ share

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Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields

This work presents a first evaluation of using spatio-temporal receptive...
10/13/2017 ∙ by Ylva Jansson, et al. ∙ 0 ∙ share

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Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing

We present a systematic analysis on the performance of a phonetic recogn...
06/29/2016 ∙ by Akash Kumar Dhaka, et al. ∙ 0 ∙ share

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Dense scale selection over space, time and space-time

Scale selection methods based on local extrema over scale of scale-norma...
09/25/2017 ∙ by Tony Lindeberg, et al. ∙ 0 ∙ share

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Fitting Generalized Essential Matrices from Generic 6x6 Matrices and its Applications

This paper addresses the problem of finding the closest generalized esse...
09/19/2017 ∙ by João R. Cardoso, et al. ∙ 0 ∙ share

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A Connectedness Constraint for Learning Sparse Graphs

Graphs are naturally sparse objects that are used to study many problems...
08/29/2017 ∙ by Martin Sundin, et al. ∙ 0 ∙ share

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Solving ill-posed inverse problems using iterative deep neural networks

We propose a partially learned approach for the solution of ill posed in...
04/13/2017 ∙ by Jonas Adler, et al. ∙ 0 ∙ share

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Determinantal Point Processes for Mini-Batch Diversification

We study a mini-batch diversification scheme for stochastic gradient des...
05/01/2017 ∙ by Cheng Zhang, et al. ∙ 0 ∙ share

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cGAN-based Manga Colorization Using a Single Training Image

The Japanese comic format known as Manga is popular all over the world. ...
06/21/2017 ∙ by Paulina Hensman, et al. ∙ 0 ∙ share

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Indirect Image Registration with Large Diffeomorphic Deformations

The paper adapts the large deformation diffeomorphic metric mapping fram...
06/13/2017 ∙ by Chong Chen, et al. ∙ 0 ∙ share

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Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection

Despite significant progress in the development of human action detectio...
06/09/2017 ∙ by Mohammadamin Barekatain, et al. ∙ 0 ∙ share

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Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks

Real-time visual analysis tasks, like tracking and recognition, require ...
05/15/2017 ∙ by Emil Eriksson, et al. ∙ 0 ∙ share

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Semi-supervised Learning with Sparse Autoencoders in Phone Classification

We propose the application of a semi-supervised learning method to impro...
10/03/2016 ∙ by Akash Kumar Dhaka, et al. ∙ 0 ∙ share

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A new kernel-based approach to system identification with quantized output data

In this paper we introduce a novel method for linear system identificati...
10/03/2016 ∙ by Giulio Bottegal, et al. ∙ 0 ∙ share

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What is the plausibility of probability?(revised 2003, 2015)

We present and examine a result related to uncertainty reasoning, namely...
11/23/2015 ∙ by Stefan Arnborg, et al. ∙ 0 ∙ share

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Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration

Fluent and safe interactions of humans and robots require both partners ...
02/27/2017 ∙ by Judith Butepage, et al. ∙ 0 ∙ share

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