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2020 – today
- 2024
[c46]Kaiting Liu, Zahra Atashgahi, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks. AISTATS 2024: 3952-3960- [c45]Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu:
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. AAMAS 2024: 733-742 - [c44]Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar:
Unveiling the Power of Sparse Neural Networks for Feature Selection. ECAI 2024: 2669-2676 - [c43]Zahra Atashgahi, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu:
Adaptive Sparsity Level During Training for Efficient Time Series Forecasting with Transformers. ECML/PKDD (1) 2024: 3-20 - [i50]Murat Onur Yildirim, Elif Ceren Gok Yildirim, Decebal Constantin Mocanu, Joaquin Vanschoren:
FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts. CoRR abs/2403.14684 (2024) - [i49]Calarina Muslimani, Bram Grooten, Deepak Ranganatha Sastry Mamillapalli, Mykola Pechenizkiy, Decebal Constantin Mocanu, Matthew E. Taylor:
Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity. CoRR abs/2406.06495 (2024) - [i48]Qiao Xiao, Pingchuan Ma, Adriana Fernandez-Lopez, Boqian Wu, Lu Yin, Stavros Petridis, Mykola Pechenizkiy, Maja Pantic, Decebal Constantin Mocanu, Shiwei Liu:
Dynamic Data Pruning for Automatic Speech Recognition. CoRR abs/2406.18373 (2024) - [i47]Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar:
Unveiling the Power of Sparse Neural Networks for Feature Selection. CoRR abs/2408.04583 (2024) - [i46]Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Are Sparse Neural Networks Better Hard Sample Learners? CoRR abs/2409.09196 (2024) - [i45]Boqian Wu, Qiao Xiao, Shunxin Wang, Nicola Strisciuglio, Mykola Pechenizkiy, Maurice van Keulen, Decebal Constantin Mocanu, Elena Mocanu:
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness. CoRR abs/2410.03030 (2024) - [i44]Camiel Oerlemans, Bram Grooten, Michiel Braat, Alaa Alassi, Emilia Silvas, Decebal Constantin Mocanu:
LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration. CoRR abs/2410.15819 (2024) - 2023
- [j19]Karine Miras
, Decebal Constantin Mocanu, A. E. Eiben:
Hu-bot: promoting the cooperation between humans and mobile robots. Neural Comput. Appl. 35(23): 16841-16852 (2023) - [j18]Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu:
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c42]Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. AAMAS 2023: 1932-1941 - [c41]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Tommi Kärkkäinen, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang:
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. ICLR 2023 - [c40]Aleksandra Nowak, Bram Grooten, Decebal Constantin Mocanu, Jacek Tabor:
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training. NeurIPS 2023 - [i43]Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. CoRR abs/2302.06548 (2023) - [i42]Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu:
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. CoRR abs/2303.07200 (2023) - [i41]Zahra Atashgahi, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu:
Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers. CoRR abs/2305.18382 (2023) - [i40]Aleksandra Nowak, Bram Grooten, Decebal Constantin Mocanu, Jacek Tabor:
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training. CoRR abs/2306.12230 (2023) - [i39]Murat Onur Yildirim, Elif Ceren Gok Yildirim, Ghada Sokar, Decebal Constantin Mocanu, Joaquin Vanschoren:
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates. CoRR abs/2308.14831 (2023) - [i38]Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu:
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. CoRR abs/2312.04727 (2023) - [i37]Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu:
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. CoRR abs/2312.15339 (2023) - 2022
- [j17]Zahra Atashgahi, Ghada Sokar, Tim van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond N. J. Veldhuis, Mykola Pechenizkiy:
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders. Mach. Learn. 111(1): 377-414 (2022) - [j16]Zahra Atashgahi, Joost Pieterse, Shiwei Liu, Decebal Constantin Mocanu, Raymond N. J. Veldhuis, Mykola Pechenizkiy:
A brain-inspired algorithm for training highly sparse neural networks. Mach. Learn. 111(12): 4411-4452 (2022) - [j15]Manuel Muñoz Sánchez, Denis Pogosov, Emilia Silvas, Decebal Constantin Mocanu, Jos Elfring, René van de Molengraft:
Situation-Aware Drivable Space Estimation for Automated Driving. IEEE Trans. Intell. Transp. Syst. 23(7): 9615-9629 (2022) - [c39]Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. ICLR 2022 - [c38]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy:
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. ICLR 2022 - [c37]Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, Peter Stone:
Dynamic Sparse Training for Deep Reinforcement Learning. IJCAI 2022: 3437-3443 - [c36]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. LoG 2022: 8 - [c35]Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Where to Pay Attention in Sparse Training for Feature Selection? NeurIPS 2022 - [c34]Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu:
Dynamic Sparse Network for Time Series Classification: Learning What to "See". NeurIPS 2022 - [c33]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks. ECML/PKDD (3) 2022: 85-101 - [i36]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy:
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. CoRR abs/2202.02643 (2022) - [i35]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu:
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. CoRR abs/2205.15322 (2022) - [i34]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang:
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. CoRR abs/2207.03620 (2022) - [i33]Zahra Atashgahi, Decebal Constantin Mocanu, Raymond N. J. Veldhuis, Mykola Pechenizkiy:
Memory-free Online Change-point Detection: A Novel Neural Network Approach. CoRR abs/2207.03932 (2022) - [i32]Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Where to Pay Attention in Sparse Training for Feature Selection? CoRR abs/2211.14627 (2022) - [i31]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. CoRR abs/2211.15335 (2022) - [i30]Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, Decebal Constantin Mocanu:
Dynamic Sparse Network for Time Series Classification: Learning What to "see". CoRR abs/2212.09840 (2022) - 2021
- [j14]Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy:
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. Evol. Comput. 29(3): 391-414 (2021) - [j13]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
SpaceNet: Make Free Space for Continual Learning. Neurocomputing 439: 1-11 (2021) - [j12]Shiwei Liu, Decebal Constantin Mocanu, Amarsagar Reddy Ramapuram Matavalam, Yulong Pei, Mykola Pechenizkiy:
Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. Neural Comput. Appl. 33(7): 2589-2604 (2021) - [j11]Shiwei Liu, Iftitahu Ni'mah, Vlado Menkovski, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Efficient and effective training of sparse recurrent neural networks. Neural Comput. Appl. 33(15): 9625-9636 (2021) - [c32]Decebal Constantin Mocanu, Elena Mocanu, Tiago Pinto, Selima Curci, Phuong H. Nguyen, Madeleine Gibescu, Damien Ernst, Zita A. Vale:
Sparse Training Theory for Scalable and Efficient Agents. AAMAS 2021: 34-38 - [c31]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Self-Attention Meta-Learner for Continual Learning. AAMAS 2021: 1658-1660 - [c30]Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy:
Selfish Sparse RNN Training. ICML 2021: 6893-6904 - [c29]Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. ICML 2021: 6989-7000 - [c28]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. NeurIPS 2021: 9908-9922 - [i29]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Learning Invariant Representation for Continual Learning. CoRR abs/2101.06162 (2021) - [i28]Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy:
Selfish Sparse RNN Training. CoRR abs/2101.09048 (2021) - [i27]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Self-Attention Meta-Learner for Continual Learning. CoRR abs/2101.12136 (2021) - [i26]Selima Curci, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Truly Sparse Neural Networks at Scale. CoRR abs/2102.01732 (2021) - [i25]Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. CoRR abs/2102.02887 (2021) - [i24]Decebal Constantin Mocanu, Elena Mocanu, Tiago Pinto, Selima Curci, Phuong H. Nguyen, Madeleine Gibescu, Damien Ernst, Zita A. Vale:
Sparse Training Theory for Scalable and Efficient Agents. CoRR abs/2103.01636 (2021) - [i23]Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, Peter Stone:
Dynamic Sparse Training for Deep Reinforcement Learning. CoRR abs/2106.04217 (2021) - [i22]Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. CoRR abs/2106.10404 (2021) - [i21]Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu:
FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. CoRR abs/2106.14568 (2021) - [i20]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Addressing the Stability-Plasticity Dilemma via Knowledge-Aware Continual Learning. CoRR abs/2110.05329 (2021) - 2020
- [c27]Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy:
Novelty producing synaptic plasticity. GECCO Companion 2020: 93-94 - [c26]Shiwei Liu, Tim van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Topological Insights into Sparse Neural Networks. ECML/PKDD (3) 2020: 279-294 - [c25]Manuel Muñoz Sánchez, Emilia Silvas, Denis Pogosov, Decebal Constantin Mocanu:
A Hybrid Framework Combining Vehicle System Knowledge with Machine Learning Methods for Improved Highway Trajectory Prediction. SMC 2020: 444-450 - [i19]Sibylle Hess, Wouter Duivesteijn, Decebal Constantin Mocanu:
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring. CoRR abs/2001.01987 (2020) - [i18]Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George H. L. Fletcher, Mykola Pechenizkiy:
Novelty Producing Synaptic Plasticity. CoRR abs/2002.03620 (2020) - [i17]Shiwei Liu, Tim van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Topological Insights in Sparse Neural Networks. CoRR abs/2006.14085 (2020) - [i16]Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
SpaceNet: Make Free Space For Continual Learning. CoRR abs/2007.07617 (2020) - [i15]Zahra Atashgahi, Ghada Sokar, Tim van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond N. J. Veldhuis, Mykola Pechenizkiy:
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders. CoRR abs/2012.00560 (2020)
2010 – 2019
- 2019
- [j10]Francesco Cauteruccio, Giancarlo Fortino, Antonio Guerrieri, Antonio Liotta, Decebal Constantin Mocanu, Cristian Perra, Giorgio Terracina, Maria Torres Vega:
Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance. Inf. Fusion 52: 13-30 (2019) - [j9]Elena Mocanu, Decebal Constantin Mocanu, Phuong H. Nguyen, Antonio Liotta, Michael E. Webber, Madeleine Gibescu, Johannes G. Slootweg:
On-Line Building Energy Optimization Using Deep Reinforcement Learning. IEEE Trans. Smart Grid 10(4): 3698-3708 (2019) - [c24]Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George H. L. Fletcher, Mykola Pechenizkiy:
Learning with delayed synaptic plasticity. GECCO 2019: 152-160 - [i14]Shiwei Liu, Decebal Constantin Mocanu, Amarsagar Reddy Ramapuram Matavalam, Yulong Pei, Mykola Pechenizkiy:
Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware. CoRR abs/1901.09181 (2019) - [i13]Shiwei Liu, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Intrinsically Sparse Long Short-Term Memory Networks. CoRR abs/1901.09208 (2019) - [i12]Joost Pieterse, Decebal Constantin Mocanu:
Evolving and Understanding Sparse Deep Neural Networks using Cosine Similarity. CoRR abs/1903.07138 (2019) - [i11]Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George H. L. Fletcher, Mykola Pechenizkiy:
Learning with Delayed Synaptic Plasticity. CoRR abs/1903.09393 (2019) - [i10]Anil Yaman, Decebal Constantin Mocanu, Giovanni Iacca, Matt Coler, George H. L. Fletcher, Mykola Pechenizkiy:
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. CoRR abs/1904.01709 (2019) - [i9]Shiwei Liu, Decebal Constantin Mocanu, Mykola Pechenizkiy:
On improving deep learning generalization with adaptive sparse connectivity. CoRR abs/1906.11626 (2019) - 2018
- [c23]Decebal Constantin Mocanu, Elena Mocanu:
One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach. AAMAS 2018: 2016-2018 - [c22]Anil Yaman, Decebal Constantin Mocanu, Giovanni Iacca, George H. L. Fletcher, Mykola Pechenizkiy:
Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution. GECCO 2018: 569-576 - [c21]Decebal Constantin Mocanu:
Synopsis of the PhD Thesis - Network Computations in Artificial Intelligence. ITC (1) 2018: 117-122 - [i8]Anil Yaman, Decebal Constantin Mocanu, Giovanni Iacca, George H. L. Fletcher, Mykola Pechenizkiy:
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution. CoRR abs/1804.07234 (2018) - [i7]Decebal Constantin Mocanu, Elena Mocanu:
One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach. CoRR abs/1804.07645 (2018) - 2017
- [j8]Maria Torres Vega, Decebal Constantin Mocanu, Antonio Liotta:
Unsupervised deep learning for real-time assessment of video streaming services. Multim. Tools Appl. 76(21): 22303-22327 (2017) - [j7]Decebal Constantin Mocanu, Haitham Bou-Ammar, Luis Puig, Eric Eaton, Antonio Liotta:
Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. Pattern Recognit. 69: 325-335 (2017) - [j6]Maria Torres Vega, Decebal Constantin Mocanu, Stavros Stavrou, Antonio Liotta:
Predictive no-reference assessment of video quality. Signal Process. Image Commun. 52: 20-32 (2017) - [j5]Maria Torres Vega, Decebal Constantin Mocanu, Jeroen Famaey, Stavros Stavrou, Antonio Liotta:
Deep Learning for Quality Assessment in Live Video Streaming. IEEE Signal Process. Lett. 24(6): 736-740 (2017) - [c20]Maria Torres Vega, Cristian Perra, Decebal Constantin Mocanu, Antonio Liotta:
Effect of lossy networks on stereoscopic 3D-video streams. BMSB 2017: 1-4 - [i6]Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta:
Evolutionary Training of Sparse Artificial Neural Networks: A Network Science Perspective. CoRR abs/1707.04780 (2017) - [i5]Elena Mocanu, Decebal Constantin Mocanu, Phuong H. Nguyen, Antonio Liotta, Michael E. Webber, Madeleine Gibescu, Johannes G. Slootweg:
On-line Building Energy Optimization using Deep Reinforcement Learning. CoRR abs/1707.05878 (2017) - 2016
- [j4]Maria Torres Vega, Vittorio Sguazzo, Decebal Constantin Mocanu, Antonio Liotta:
An experimental survey of no-reference video quality assessment methods. Int. J. Pervasive Comput. Commun. 12(1): 66-86 (2016) - [j3]Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta:
A topological insight into restricted Boltzmann machines. Mach. Learn. 104(2-3): 243-270 (2016) - [c19]Decebal Constantin Mocanu:
On the Synergy of Network Science and Artificial Intelligence. IJCAI 2016: 4020-4021 - [c18]Michele Chincoli, Aly Aamer Syed, Decebal Constantin Mocanu, Antonio Liotta:
Predictive Power Control in Wireless Sensor Networks. IoTDI 2016: 309-312 - [c17]Maria Torres Vega, Decebal Constantin Mocanu, Antonio Liotta:
A Regression Method for real-time video quality evaluation. MoMM 2016: 217-224 - [c16]Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta:
Big IoT data mining for real-time energy disaggregation in buildings. SMC 2016: 3765-3769 - [c15]Kathrin Borchert, Matthias Hirth, Thomas Zinner, Decebal Constantin Mocanu:
Correlating QoE and Technical Parameters of an SAP System in an Enterprise Environment. QCMan@ITC 2016: 34-36 - [i4]Decebal Constantin Mocanu, Haitham Bou-Ammar, Luis Puig, Eric Eaton, Antonio Liotta:
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines. CoRR abs/1604.05865 (2016) - [i3]Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta:
A topological insight into restricted Boltzmann machines. CoRR abs/1604.05978 (2016) - [i2]Maria Torres Vega, Decebal Constantin Mocanu, Antonio Liotta:
Predictive No-Reference Assessment of Video Quality. CoRR abs/1604.07322 (2016) - [i1]Decebal Constantin Mocanu, Maria Torres Vega, Eric Eaton, Peter Stone, Antonio Liotta:
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data. CoRR abs/1610.05555 (2016) - 2015
- [j2]Decebal Constantin Mocanu, Jeevan Pokhrel, Juan Pablo Garella, Janne Seppänen, Eirini Liotou, Manish Narwaria:
No-reference video quality measurement: added value of machine learning. J. Electronic Imaging 24(6): 061208 (2015) - [j1]Decebal Constantin Mocanu, Haitham Bou-Ammar, Dietwig Lowet, Kurt Driessens, Antonio Liotta, Gerhard Weiss, Karl Tuyls:
Factored four way conditional restricted Boltzmann machines for activity recognition. Pattern Recognit. Lett. 66: 100-108 (2015) - [c14]Decebal Constantin Mocanu, Maria Torres Vega, Antonio Liotta:
Redundancy Reduction in Wireless Sensor Networks via Centrality Metrics. ICDM Workshops 2015: 501-507 - [c13]Decebal Constantin Mocanu, Georgios Exarchakos, Haitham Bou-Ammar, Antonio Liotta:
Reduced reference image quality assessment via Boltzmann Machines. IM 2015: 1278-1281 - [c12]Maria Torres Vega, Decebal Constantin Mocanu, Rosario Barresi, Giancarlo Fortino, Antonio Liotta:
Cognitive streaming on android devices. IM 2015: 1316-1321 - [c11]Maria Torres Vega, Vittorio Sguazzo, Decebal Constantin Mocanu, Antonio Liotta:
Accuracy of No-Reference Quality Metrics in Network-impaired Video Streams. MoMM 2015: 326-333 - [c10]Maria Torres Vega, Emanuele Giordano, Decebal Constantin Mocanu, Dian Tjondronegoro, Antonio Liotta:
Cognitive no-reference video quality assessment for mobile streaming services. QoMEX 2015: 1-6 - 2014
- [c9]Decebal Constantin Mocanu, Giuliano Santandrea, Walter Cerroni, Franco Callegati, Antonio Liotta:
Network performance assessment with Quality of experience benchmarks. CNSM 2014: 332-335 - [c8]Maria Torres Vega, Shihuan Zou, Decebal Constantin Mocanu, Eduward Tangdiongga, Antonius M. J. Koonen, Antonio Liotta:
End-to-end performance evaluation in high-speed wireless networks. CNSM 2014: 344-347 - [c7]Decebal Constantin Mocanu, Georgios Exarchakos, Antonio Liotta:
Deep learning for objective quality assessment of 3D images. ICIP 2014: 758-762 - [c6]Decebal Constantin Mocanu, Antonio Liotta, Arianna Ricci, Maria Torres Vega, Georgios Exarchakos:
When does lower bitrate give higher quality in modern video services? NOMS 2014: 1-5 - [c5]Elena Mocanu, Decebal Constantin Mocanu, Haitham Bou-Ammar, Zoran Zivkovic, Antonio Liotta, Evgueni N. Smirnov:
Inexpensive user tracking using Boltzmann Machines. SMC 2014: 1-6 - [c4]Decebal Constantin Mocanu, Georgios Exarchakos, Antonio Liotta:
Node centrality awareness via swarming effects. SMC 2014: 19-24 - 2013
- [c3]Roshan Kotian, Georgios Exarchakos, Decebal Constantin Mocanu, Antonio Liotta:
Predicting Battery Depletion of Neighboring Wireless Sensor Nodes. ICA3PP (2) 2013: 276-284 - [c2]Antonio Liotta, Decebal Constantin Mocanu, Vlado Menkovski, Luciana Cagnetta, Georgios Exarchakos:
Instantaneous Video Quality Assessment for lightweight devices. MoMM 2013: 525 - [c1]Haitham Bou-Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens, Karl Tuyls, Gerhard Weiss:
Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines. ECML/PKDD (2) 2013: 449-464
Coauthor Index
[c46] [c45] [c44] [c43] [i49] [i48] [i47] [i46] [i45] [j18] [c42] [c41] [i43] [i42] [i41] [i38] [i37] [j17] [j16] [c39] [c38] [c37] [c36] [c35] [c34] [c33] [i36] [i35] [i34] [i33] [i32] [i31] [i30] [j14] [j13] [j12] [j11] [c31] [c30] [c29] [c28] [i29] [i28] [i27] [i26] [i25] [i23] [i22] [i21] [i20] [c27] [c26] [i18] [i17] [i16] [i15] [c24] [i14] [i13] [i11] [i10] [i9] [c22] [i8]
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last updated on 2024-11-27 21:19 CET by the dblp team
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