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Other applications of machine learning (MLR-APPL)

Graph Signal Sampling via Reinforcement Learning

Oleksii Abramenko
Alexander Jung

We model the sampling and recovery of clustered graph signals as a reinforcement learning (RL) problem. The signal sampling is carried out by an agent which crawls over the graph and selects the most relevant graph nodes to sample. The goal of the agent is to select signal samples which allow for the most accurate recovery. The sample selection is formulated as a multi-armed bandit (MAB) problem, which lends naturally to learning efficient sampling strategies using the well-known gradient MAB algorithm.

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Authors:
Oleksii Abramenko
Alexander Jung
Oleksii Abramenko, Alexander Jung
Submitted On:
30 May 2019 - 10:50am
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[1] Oleksii Abramenko, Alexander Jung, "Graph Signal Sampling via Reinforcement Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4562. Accessed: Jul. 18, 2019.
@article{4562-19,
url = {http://sigport.org/4562},
author = {Oleksii Abramenko; Alexander Jung },
publisher = {IEEE SigPort},
title = {Graph Signal Sampling via Reinforcement Learning},
year = {2019} }
TY - EJOUR
T1 - Graph Signal Sampling via Reinforcement Learning
AU - Oleksii Abramenko; Alexander Jung
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4562
ER -
Oleksii Abramenko, Alexander Jung. (2019). Graph Signal Sampling via Reinforcement Learning. IEEE SigPort. http://sigport.org/4562
Oleksii Abramenko, Alexander Jung, 2019. Graph Signal Sampling via Reinforcement Learning. Available at: http://sigport.org/4562.
Oleksii Abramenko, Alexander Jung. (2019). "Graph Signal Sampling via Reinforcement Learning." Web.
1. Oleksii Abramenko, Alexander Jung. Graph Signal Sampling via Reinforcement Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4562

FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES

Shaojie Xu

Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a generative model. We search and constrain on latent variable space to make the method stable when the number of compressed measurements is extremely limited. We show that, by exploiting certain structures of the latent variables, the proposed method produces improved reconstruction accuracy and preserves realistic and non-smooth features in the image.

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Authors:
Shaojie Xu
Submitted On:
12 May 2019 - 12:59pm
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Xu, Shaojie ICCASP 2019 Presentation Slides.pdf

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[1] , "FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4467. Accessed: Jul. 18, 2019.
@article{4467-19,
url = {http://sigport.org/4467},
author = { },
publisher = {IEEE SigPort},
title = {FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES},
year = {2019} }
TY - EJOUR
T1 - FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4467
ER -
. (2019). FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES. IEEE SigPort. http://sigport.org/4467
, 2019. FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES. Available at: http://sigport.org/4467.
. (2019). "FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES." Web.
1. . FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4467

Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes

Juliette Valenchon

Improving disease outcome prediction can greatly aid in the strategic deployment of secondary prevention approaches. We develop a method to predict the evolution of diseases by taking into account personal attributes of the subjects and their relationships with medical examination results. Our approach builds upon a recent formulation of this problem as a graph-based geometric matrix completion task. The primary innovation is the introduction of multiple graphs, each relying on a different combination of subject attributes.

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Authors:
Juliette Valenchon
Juliette Valenchon, Mark Coates
Submitted On:
11 May 2019 - 1:04pm
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Poster ICASSP 2019

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[1] Juliette Valenchon, Mark Coates, "Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4449. Accessed: Jul. 18, 2019.
@article{4449-19,
url = {http://sigport.org/4449},
author = {Juliette Valenchon; Mark Coates },
publisher = {IEEE SigPort},
title = {Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes},
year = {2019} }
TY - EJOUR
T1 - Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes
AU - Juliette Valenchon; Mark Coates
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4449
ER -
Juliette Valenchon, Mark Coates. (2019). Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes. IEEE SigPort. http://sigport.org/4449
Juliette Valenchon, Mark Coates, 2019. Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes. Available at: http://sigport.org/4449.
Juliette Valenchon, Mark Coates. (2019). "Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes." Web.
1. Juliette Valenchon, Mark Coates. Multiple-Graph Recurrent Graph Convolutional Neural Network architectures for predicting disease outcomes [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4449

MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE

Tien Do
Duc Nguyen

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations.

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Authors:
Tien Do
Duc Nguyen
Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis
Submitted On:
11 May 2019 - 1:38am
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icassp19_poster.pdf

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[1] Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis, "MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4435. Accessed: Jul. 18, 2019.
@article{4435-19,
url = {http://sigport.org/4435},
author = {Evaggelia Tsiligianni; Angel Lopez Aguirre; Valerio Panzica La Manna; Frank Pasveer; Wilfried Philips; Nikos Deligiannis },
publisher = {IEEE SigPort},
title = {MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE},
year = {2019} }
TY - EJOUR
T1 - MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE
AU - Evaggelia Tsiligianni; Angel Lopez Aguirre; Valerio Panzica La Manna; Frank Pasveer; Wilfried Philips; Nikos Deligiannis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4435
ER -
Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis. (2019). MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE. IEEE SigPort. http://sigport.org/4435
Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis, 2019. MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE. Available at: http://sigport.org/4435.
Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis. (2019). "MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE." Web.
1. Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis. MATRIX COMPLETION WITH VARIATIONAL GRAPH AUTOENCODERS: APPLICATION IN HYPERLOCAL AIR QUALITY INFERENCE [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4435

[Slides] Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart

Trung Vu

This paper introduces the use of adaptive restart to accelerate iterative hard thresholding (IHT) for low-rank matrix completion. First, we analyze the local convergence of accelerated IHT in the non-convex setting of matrix completion problem (MCP). We prove the linear convergence rate of the accelerated algorithm inside the region near the solution. Our analysis poses a major challenge to parameter selection for accelerated IHT when no prior knowledge of the "local Hessian condition number" is given.

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Authors:
Trung Vu
Trung Vu, Raviv Raich
Submitted On:
10 May 2019 - 4:04pm
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[1] Trung Vu, Raviv Raich, "[Slides] Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4400. Accessed: Jul. 18, 2019.
@article{4400-19,
url = {http://sigport.org/4400},
author = {Trung Vu; Raviv Raich },
publisher = {IEEE SigPort},
title = {[Slides] Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart},
year = {2019} }
TY - EJOUR
T1 - [Slides] Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart
AU - Trung Vu; Raviv Raich
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4400
ER -
Trung Vu, Raviv Raich. (2019). [Slides] Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart. IEEE SigPort. http://sigport.org/4400
Trung Vu, Raviv Raich, 2019. [Slides] Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart. Available at: http://sigport.org/4400.
Trung Vu, Raviv Raich. (2019). "[Slides] Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart." Web.
1. Trung Vu, Raviv Raich. [Slides] Accelerating Iterative Hard Thresholding for Low-rank Matrix Completion via Adaptive Restart [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4400

[Poster] Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion

Trung Vu

We present a momentum-based accelerated iterative hard thresholding (IHT) for low-rank matrix completion. We analyze the convergence of the proposed Heavy Ball (HB) accelerated IHT near the solution and provide optimal step size parameters that guarantee the fastest rate of convergence. Since the optimal step sizes depend on the unknown structure of the solution matrix, we further propose a heuristic for parameter selection that is inspired by recent results in random matrix theory.

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Authors:
Trung Vu
Trung Vu, Raviv Raich
Submitted On:
10 May 2019 - 4:00pm
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[1] Trung Vu, Raviv Raich, "[Poster] Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4397. Accessed: Jul. 18, 2019.
@article{4397-19,
url = {http://sigport.org/4397},
author = {Trung Vu; Raviv Raich },
publisher = {IEEE SigPort},
title = {[Poster] Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion},
year = {2019} }
TY - EJOUR
T1 - [Poster] Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion
AU - Trung Vu; Raviv Raich
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4397
ER -
Trung Vu, Raviv Raich. (2019). [Poster] Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion. IEEE SigPort. http://sigport.org/4397
Trung Vu, Raviv Raich, 2019. [Poster] Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion. Available at: http://sigport.org/4397.
Trung Vu, Raviv Raich. (2019). "[Poster] Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion." Web.
1. Trung Vu, Raviv Raich. [Poster] Local Convergence of the Heavy Ball method in Iterative Hard Thresholding for Low-Rank Matrix Completion [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4397

Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks

Navneet Garg

To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network.

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Authors:
Navneet Garg
Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah
Submitted On:
10 May 2019 - 7:46am
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posterq2.pdf

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[1] Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah, "Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4312. Accessed: Jul. 18, 2019.
@article{4312-19,
url = {http://sigport.org/4312},
author = {Navneet Garg; Mathini Sellathurai; Tharmalingam Ratnarajah },
publisher = {IEEE SigPort},
title = {Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks},
year = {2019} }
TY - EJOUR
T1 - Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks
AU - Navneet Garg; Mathini Sellathurai; Tharmalingam Ratnarajah
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4312
ER -
Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah. (2019). Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks. IEEE SigPort. http://sigport.org/4312
Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah, 2019. Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks. Available at: http://sigport.org/4312.
Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah. (2019). "Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks." Web.
1. Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah. Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4312

Generalized Boundary Detection Using Compression-Based Analytics

Richard Field

We present a new method for boundary detection within sequential data using compression-based analytics. Our approach is to approximate the information distance between two adjacent sliding windows within the sequence. Large values in the distance metric are indicative of boundary locations. A new algorithm is developed, referred to as sliding information distance (SLID), that provides a fast, accurate, and robust approximation to the normalized information distance. A modified smoothed z-score algorithm is used to locate peaks in the distance metric, indicating boundary locations.

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Authors:
Richard Field
Christina Ting, Tu-Thach Quach, Travis Bauer
Submitted On:
8 May 2019 - 12:13pm
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[1] Christina Ting, Tu-Thach Quach, Travis Bauer, " Generalized Boundary Detection Using Compression-Based Analytics", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4120. Accessed: Jul. 18, 2019.
@article{4120-19,
url = {http://sigport.org/4120},
author = {Christina Ting; Tu-Thach Quach; Travis Bauer },
publisher = {IEEE SigPort},
title = { Generalized Boundary Detection Using Compression-Based Analytics},
year = {2019} }
TY - EJOUR
T1 - Generalized Boundary Detection Using Compression-Based Analytics
AU - Christina Ting; Tu-Thach Quach; Travis Bauer
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4120
ER -
Christina Ting, Tu-Thach Quach, Travis Bauer. (2019). Generalized Boundary Detection Using Compression-Based Analytics. IEEE SigPort. http://sigport.org/4120
Christina Ting, Tu-Thach Quach, Travis Bauer, 2019. Generalized Boundary Detection Using Compression-Based Analytics. Available at: http://sigport.org/4120.
Christina Ting, Tu-Thach Quach, Travis Bauer. (2019). " Generalized Boundary Detection Using Compression-Based Analytics." Web.
1. Christina Ting, Tu-Thach Quach, Travis Bauer. Generalized Boundary Detection Using Compression-Based Analytics [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4120

DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS

Hafiz Imtiaz
Anand Sarwate

We propose a distributed differentially-private canonical correlation analysis (CCA) algorithm to use on multi-view data. CCA finds a subspace for each view such that projecting the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. In applications involving privacy-sensitive data, such as medical imaging, distributed privacy-preserving algorithms can let data holders maintain local control of their data while participating in joint computations with other data holders.

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Authors:
Hafiz Imtiaz
Anand Sarwate
Hafiz Imtiaz, Anand D. Sarwate
Submitted On:
8 May 2019 - 10:10am
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Imtiaz_Sarwate_ICASSP2019_Poster_ver2.pdf

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[1] Hafiz Imtiaz, Anand D. Sarwate, "DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4114. Accessed: Jul. 18, 2019.
@article{4114-19,
url = {http://sigport.org/4114},
author = {Hafiz Imtiaz; Anand D. Sarwate },
publisher = {IEEE SigPort},
title = {DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS},
year = {2019} }
TY - EJOUR
T1 - DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS
AU - Hafiz Imtiaz; Anand D. Sarwate
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4114
ER -
Hafiz Imtiaz, Anand D. Sarwate. (2019). DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS. IEEE SigPort. http://sigport.org/4114
Hafiz Imtiaz, Anand D. Sarwate, 2019. DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS. Available at: http://sigport.org/4114.
Hafiz Imtiaz, Anand D. Sarwate. (2019). "DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS." Web.
1. Hafiz Imtiaz, Anand D. Sarwate. DISTRIBUTED DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4114

CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS

Guilherme Correa
Bruno Zatt

Video transrating has become an essential task in streaming service providers that need to transmit and deliver different versions of the same content for a multitude of users that operate under different network conditions. As the transrating operation is comprised of a decoding and an encoding step in sequence, a huge computational cost is required in such large-scale services, especially when considering the use of complex state-of-the-art codecs, such as the High Efficiency Video Coding (HEVC).

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Authors:
Guilherme Correa
Bruno Zatt
Thiago Bubolz, Mateus Grellert
Submitted On:
7 May 2019 - 3:15pm
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PosterICASSP_vF.pdf

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[1] Thiago Bubolz, Mateus Grellert, "CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3951. Accessed: Jul. 18, 2019.
@article{3951-19,
url = {http://sigport.org/3951},
author = {Thiago Bubolz; Mateus Grellert },
publisher = {IEEE SigPort},
title = {CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS},
year = {2019} }
TY - EJOUR
T1 - CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS
AU - Thiago Bubolz; Mateus Grellert
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3951
ER -
Thiago Bubolz, Mateus Grellert. (2019). CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS. IEEE SigPort. http://sigport.org/3951
Thiago Bubolz, Mateus Grellert, 2019. CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS. Available at: http://sigport.org/3951.
Thiago Bubolz, Mateus Grellert. (2019). "CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS." Web.
1. Thiago Bubolz, Mateus Grellert. CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3951

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