Predicting Battery Depletion of Neighboring Wireless Sensor Nodes. - Roshan Kotian; Georgios Exarchakos; Decebal Constantin Mocanu; Antonio Liotta (ICA3PP (2), 2013)

Instantaneous Video Quality Assessment for lightweight devices. - Antonio Liotta; Decebal Constantin Mocanu; Vlado Menkovski; Luciana Cagnetta; Georgios Exarchakos (MoMM, 2013)

Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines. - Haitham Bou-Ammar; Decebal Constantin Mocanu; Matthew E. Taylor; Kurt Driessens; Karl Tuyls; Gerhard Weiss 001 (ECML/PKDD (2), 2013)

Network performance assessment with Quality of experience benchmarks. - Decebal Constantin Mocanu; Giuliano Santandrea; Walter Cerroni; Franco Callegati; Antonio Liotta (CNSM, 2014)

End-to-end performance evaluation in high-speed wireless networks. - Maria Torres Vega; Shihuan Zou; Decebal Constantin Mocanu; Eduward Tangdiongga; Antonius M. J. Koonen; Antonio Liotta (CNSM, 2014)

Deep learning for objective quality assessment of 3D images. - Decebal Constantin Mocanu; Georgios Exarchakos; Antonio Liotta (ICIP, 2014)

When does lower bitrate give higher quality in modern video services? - Decebal Constantin Mocanu; Antonio Liotta; Arianna Ricci; Maria Torres Vega; Georgios Exarchakos (NOMS, 2014)

Node centrality awareness via swarming effects. - Decebal Constantin Mocanu; Georgios Exarchakos; Antonio Liotta (SMC, 2014)

Inexpensive user tracking using Boltzmann Machines. - Elena Mocanu; Decebal Constantin Mocanu; Haitham Bou-Ammar; Zoran Zivkovic; Antonio Liotta; Evgueni N. Smirnov (SMC, 2014)

Redundancy Reduction in Wireless Sensor Networks via Centrality Metrics. - Decebal Constantin Mocanu; Maria Torres Vega; Antonio Liotta (ICDM Workshops, 2015)

Reduced reference image quality assessment via Boltzmann Machines. - Decebal Constantin Mocanu; Georgios Exarchakos; Haitham Bou-Ammar; Antonio Liotta (IM, 2015)

Cognitive streaming on android devices. - Maria Torres Vega; Decebal Constantin Mocanu; Rosario Barresi; Giancarlo Fortino; Antonio Liotta (IM, 2015)

Accuracy of No-Reference Quality Metrics in Network-impaired Video Streams. - Maria Torres Vega; Vittorio Sguazzo; Decebal Constantin Mocanu; Antonio Liotta (MoMM, 2015)

Cognitive no-reference video quality assessment for mobile streaming services. - Maria Torres Vega; Emanuele Giordano; Decebal Constantin Mocanu; Dian Tjondronegoro; Antonio Liotta (QoMEX, 2015)

No-reference video quality measurement: added value of machine learning. - Decebal Constantin Mocanu; Jeevan Pokhrel; Juan Pablo Garella; Janne Seppänen; Eirini Liotou; Manish Narwaria (J. Electronic Imaging, 2015)

Factored four way conditional restricted Boltzmann machines for activity recognition. - Decebal Constantin Mocanu; Haitham Bou-Ammar; Dietwig Lowet; Kurt Driessens; Antonio Liotta; Gerhard Weiss 001; Karl Tuyls (Pattern Recognit. Lett., 2015)

On the Synergy of Network Science and Artificial Intelligence. - Decebal Constantin Mocanu (IJCAI, 2016)

Predictive Power Control in Wireless Sensor Networks. - Michele Chincoli; Aly Aamer Syed; Decebal Constantin Mocanu; Antonio Liotta (IoTDI, 2016)

A Regression Method for real-time video quality evaluation. - Maria Torres Vega; Decebal Constantin Mocanu; Antonio Liotta (MoMM, 2016)

Big IoT data mining for real-time energy disaggregation in buildings. - Decebal Constantin Mocanu; Elena Mocanu; Phuong H. Nguyen; Madeleine Gibescu; Antonio Liotta (SMC, 2016)

Correlating QoE and Technical Parameters of an SAP System in an Enterprise Environment. - Kathrin Borchert; Matthias Hirth; Thomas Zinner; Decebal Constantin Mocanu (QCMan@ITC, 2016)

Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data. - Decebal Constantin Mocanu; Maria Torres Vega; Eric Eaton; Peter Stone 001; Antonio Liotta (CoRR, 2016)

Predictive No-Reference Assessment of Video Quality. - Maria Torres Vega; Decebal Constantin Mocanu; Antonio Liotta (CoRR, 2016)

An experimental survey of no-reference video quality assessment methods. - Maria Torres Vega; Vittorio Sguazzo; Decebal Constantin Mocanu; Antonio Liotta (Int. J. Pervasive Comput. Commun., 2016)

A topological insight into restricted Boltzmann machines. - Decebal Constantin Mocanu; Elena Mocanu; Phuong H. Nguyen; Madeleine Gibescu; Antonio Liotta (Mach. Learn., 2016)

Effect of lossy networks on stereoscopic 3D-video streams. - Maria Torres Vega; Cristian Perra; Decebal Constantin Mocanu; Antonio Liotta (BMSB, 2017)

Evolutionary Training of Sparse Artificial Neural Networks: A Network Science Perspective. - Decebal Constantin Mocanu; Elena Mocanu; Peter Stone 001; Phuong H. Nguyen; Madeleine Gibescu; Antonio Liotta (CoRR, 2017)

Unsupervised deep learning for real-time assessment of video streaming services. - Maria Torres Vega; Decebal Constantin Mocanu; Antonio Liotta (Multim. Tools Appl., 2017)

Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. - Decebal Constantin Mocanu; Haitham Bou-Ammar; Luis Puig; Eric Eaton; Antonio Liotta (Pattern Recognit., 2017)

Predictive no-reference assessment of video quality. - Maria Torres Vega; Decebal Constantin Mocanu; Stavros Stavrou; Antonio Liotta (Signal Process. Image Commun., 2017)

Deep Learning for Quality Assessment in Live Video Streaming. - Maria Torres Vega; Decebal Constantin Mocanu; Jeroen Famaey; Stavros Stavrou; Antonio Liotta (IEEE Signal Process. Lett., 2017)

One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach. - Decebal Constantin Mocanu; Elena Mocanu (AAMAS, 2018)

Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution. - Anil Yaman; Decebal Constantin Mocanu; Giovanni Iacca; George H. L. Fletcher; Mykola Pechenizkiy (GECCO, 2018)

Synopsis of the PhD Thesis - Network Computations in Artificial Intelligence. - Decebal Constantin Mocanu (ITC (1), 2018)

Learning with delayed synaptic plasticity. - Anil Yaman; Giovanni Iacca; Decebal Constantin Mocanu; George H. L. Fletcher; Mykola Pechenizkiy (GECCO, 2019)

Intrinsically Sparse Long Short-Term Memory Networks. - Shiwei Liu 003; Decebal Constantin Mocanu; Mykola Pechenizkiy (CoRR, 2019)

Evolving and Understanding Sparse Deep Neural Networks using Cosine Similarity. - Joost Pieterse; Decebal Constantin Mocanu (CoRR, 2019)

On improving deep learning generalization with adaptive sparse connectivity. - Shiwei Liu 003; Decebal Constantin Mocanu; Mykola Pechenizkiy (CoRR, 2019)

Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance. - Francesco Cauteruccio; Giancarlo Fortino; Antonio Guerrieri; Antonio Liotta; Decebal Constantin Mocanu; Cristian Perra; Giorgio Terracina; Maria Torres Vega (Inf. Fusion, 2019)

On-Line Building Energy Optimization Using Deep Reinforcement Learning. - Elena Mocanu; Decebal Constantin Mocanu; Phuong H. Nguyen; Antonio Liotta; Michael E. Webber; Madeleine Gibescu; Johannes G. Slootweg (IEEE Trans. Smart Grid, 2019)

Novelty producing synaptic plasticity. - Anil Yaman; Giovanni Iacca; Decebal Constantin Mocanu; George Fletcher 001; Mykola Pechenizkiy (GECCO Companion, 2020)

Topological Insights into Sparse Neural Networks. - Shiwei Liu 003; Tim van der Lee; Anil Yaman; Zahra Atashgahi; Davide Ferraro; Ghada Sokar; Mykola Pechenizkiy; Decebal Constantin Mocanu (ECML/PKDD (3), 2020)

A Hybrid Framework Combining Vehicle System Knowledge with Machine Learning Methods for Improved Highway Trajectory Prediction. - Manuel Muñoz Sánchez; Emilia Silvas; Denis Pogosov; Decebal Constantin Mocanu (SMC, 2020)

Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring. - Sibylle Hess; Wouter Duivesteijn; Decebal Constantin Mocanu (CoRR, 2020)

Topological Insights in Sparse Neural Networks. - Shiwei Liu 003; Tim van der Lee; Anil Yaman; Zahra Atashgahi; Davide Ferraro; Ghada Sokar; Mykola Pechenizkiy; Decebal Constantin Mocanu (CoRR, 2020)

Sparse Training Theory for Scalable and Efficient Agents. - Decebal Constantin Mocanu; Elena Mocanu; Tiago Pinto; Selima Curci; Phuong H. Nguyen; Madeleine Gibescu; Damien Ernst; Zita A. Vale (AAMAS, 2021)

Self-Attention Meta-Learner for Continual Learning. - Ghada Sokar; Decebal Constantin Mocanu; Mykola Pechenizkiy (AAMAS, 2021)

Selfish Sparse RNN Training. - Shiwei Liu 003; Decebal Constantin Mocanu; Yulong Pei; Mykola Pechenizkiy (ICML, 2021)

Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. - Shiwei Liu 003; Lu Yin 006; Decebal Constantin Mocanu; Mykola Pechenizkiy (ICML, 2021)

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen 001; Zahra Atashgahi; Lu Yin 006; Huanyu Kou; Li Shen 008; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (NeurIPS, 2021)

Learning Invariant Representation for Continual Learning. - Ghada Sokar; Decebal Constantin Mocanu; Mykola Pechenizkiy (CoRR, 2021)

Truly Sparse Neural Networks at Scale. - Selima Curci; Decebal Constantin Mocanu; Mykola Pechenizkiy (CoRR, 2021)

FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Zahra Atashgahi; Xiaohan Chen 001; Ghada Sokar; Elena Mocanu; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (CoRR, 2021)

Addressing the Stability-Plasticity Dilemma via Knowledge-Aware Continual Learning. - Ghada Sokar; Decebal Constantin Mocanu; Mykola Pechenizkiy (CoRR, 2021)

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. - Anil Yaman; Giovanni Iacca; Decebal Constantin Mocanu; Matt Coler; George Fletcher 001; Mykola Pechenizkiy (Evol. Comput., 2021)

SpaceNet: Make Free Space for Continual Learning. - Ghada Sokar; Decebal Constantin Mocanu; Mykola Pechenizkiy (Neurocomputing, 2021)

Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. - Shiwei Liu 003; Decebal Constantin Mocanu; Amarsagar Reddy Ramapuram Matavalam; Yulong Pei; Mykola Pechenizkiy (Neural Comput. Appl., 2021)

Efficient and effective training of sparse recurrent neural networks. - Shiwei Liu 003; Iftitahu Ni'mah; Vlado Menkovski; Decebal Constantin Mocanu; Mykola Pechenizkiy (Neural Comput. Appl., 2021)

Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Zahra Atashgahi; Xiaohan Chen 001; Ghada Sokar; Elena Mocanu; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (ICLR, 2022)

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen 001; Li Shen 008; Decebal Constantin Mocanu; Zhangyang Wang; Mykola Pechenizkiy (ICLR, 2022)

Dynamic Sparse Training for Deep Reinforcement Learning. - Ghada Sokar; Elena Mocanu; Decebal Constantin Mocanu; Mykola Pechenizkiy; Peter Stone 001 (IJCAI, 2022)

You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. - Tianjin Huang; Tianlong Chen 001; Meng Fang; Vlado Menkovski; Jiaxu Zhao 002; Lu Yin 006; Yulong Pei; Decebal Constantin Mocanu; Zhangyang Wang; Mykola Pechenizkiy; Shiwei Liu 003 (LoG, 2022)

Where to Pay Attention in Sparse Training for Feature Selection? - Ghada Sokar; Zahra Atashgahi; Mykola Pechenizkiy; Decebal Constantin Mocanu (NeurIPS, 2022)

Dynamic Sparse Network for Time Series Classification: Learning What to "See". - Qiao Xiao; Boqian Wu; Yu Zhang 006; Shiwei Liu 003; Mykola Pechenizkiy; Elena Mocanu; Decebal Constantin Mocanu (NeurIPS, 2022)

Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks. - Ghada Sokar; Decebal Constantin Mocanu; Mykola Pechenizkiy (ECML/PKDD (3), 2022)

Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. - Lu Yin 006; Vlado Menkovski; Meng Fang; Tianjin Huang; Yulong Pei; Mykola Pechenizkiy; Decebal Constantin Mocanu; Shiwei Liu 003 (CoRR, 2022)

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen 001; Xuxi Chen; Qiao Xiao; Boqian Wu; Mykola Pechenizkiy; Decebal Constantin Mocanu; Zhangyang Wang (CoRR, 2022)

Memory-free Online Change-point Detection: A Novel Neural Network Approach. - Zahra Atashgahi; Decebal Constantin Mocanu; Raymond N. J. Veldhuis; Mykola Pechenizkiy (CoRR, 2022)

A brain-inspired algorithm for training highly sparse neural networks. - Zahra Atashgahi; Joost Pieterse; Shiwei Liu 003; Decebal Constantin Mocanu; Raymond N. J. Veldhuis; Mykola Pechenizkiy (Mach. Learn., 2022)

Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders. - Zahra Atashgahi; Ghada Sokar; Tim van der Lee; Elena Mocanu; Decebal Constantin Mocanu; Raymond N. J. Veldhuis; Mykola Pechenizkiy (Mach. Learn., 2022)

Situation-Aware Drivable Space Estimation for Automated Driving. - Manuel Muñoz Sánchez; Denis Pogosov; Emilia Silvas; Decebal Constantin Mocanu; Jos Elfring; René van de Molengraft (IEEE Trans. Intell. Transp. Syst., 2022)

Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. - Bram Grooten; Ghada Sokar; Shibhansh Dohare; Elena Mocanu; Matthew E. Taylor; Mykola Pechenizkiy; Decebal Constantin Mocanu (AAMAS, 2023)

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen 001; Xuxi Chen; Qiao Xiao; Boqian Wu; Tommi Kärkkäinen; Mykola Pechenizkiy; Decebal Constantin Mocanu; Zhangyang Wang (ICLR, 2023)

Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training. - Aleksandra Nowak 001; Bram Grooten; Decebal Constantin Mocanu; Jacek Tabor (NeurIPS, 2023)

Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. - Zahra Atashgahi; Xuhao Zhang; Neil Kichler; Shiwei Liu 003; Lu Yin 006; Mykola Pechenizkiy; Raymond N. J. Veldhuis; Decebal Constantin Mocanu (Trans. Mach. Learn. Res., 2023)

Hu-bot: promoting the cooperation between humans and mobile robots. - Karine Miras; Decebal Constantin Mocanu; A. E. Eiben (Neural Comput. Appl., 2023)

Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks. - Kaiting Liu; Zahra Atashgahi; Ghada Sokar; Mykola Pechenizkiy; Decebal Constantin Mocanu (AISTATS, 2024)

MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. - Bram Grooten; Tristan Tomilin; Gautham Vasan; Matthew E. Taylor; A. Rupam Mahmood; Meng Fang; Mykola Pechenizkiy; Decebal Constantin Mocanu (AAMAS, 2024)

Are Sparse Neural Networks Better Hard Sample Learners? - Qiao Xiao; Boqian Wu; Lu Yin 006; Christopher Neil Gadzinski; Tianjin Huang; Mykola Pechenizkiy; Decebal Constantin Mocanu (BMVC, 2024)

Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates. - Murat Onur Yildirim; Elif Ceren Gok Yildirim; Ghada Sokar; Decebal Constantin Mocanu; Joaquin Vanschoren (CPAL, 2024)

Unveiling the Power of Sparse Neural Networks for Feature Selection. - Zahra Atashgahi; Tennison Liu; Mykola Pechenizkiy; Raymond N. J. Veldhuis; Decebal Constantin Mocanu; Mihaela van der Schaar (ECAI, 2024)

Dynamic Data Pruning for Automatic Speech Recognition. - Qiao Xiao; Pingchuan Ma 001; Adriana Fernandez-Lopez; Boqian Wu; Lu Yin 006; Stavros Petridis; Mykola Pechenizkiy; Maja Pantic; Decebal Constantin Mocanu; Shiwei Liu 003 (INTERSPEECH, 2024)

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. - Boqian Wu; Qiao Xiao; Shiwei Liu 003; Lu Yin 006; Mykola Pechenizkiy; Decebal Constantin Mocanu; Maurice van Keulen; Elena Mocanu (NeurIPS, 2024)

Adaptive Sparsity Level During Training for Efficient Time Series Forecasting with Transformers. - Zahra Atashgahi; Mykola Pechenizkiy; Raymond N. J. Veldhuis; Decebal Constantin Mocanu (ECML/PKDD (1), 2024)

FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts. - Murat Onur Yildirim; Elif Ceren Gok Yildirim; Decebal Constantin Mocanu; Joaquin Vanschoren (CoRR, 2024)

LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration. - Camiel Oerlemans; Bram Grooten; Michiel Braat; Alaa Alassi; Emilia Silvas; Decebal Constantin Mocanu (CoRR, 2024)

Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness. - Boqian Wu; Qiao Xiao; Shunxin Wang; Nicola Strisciuglio; Mykola Pechenizkiy; Maurice van Keulen; Decebal Constantin Mocanu; Elena Mocanu (ICLR, 2025)

Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity. - Calarina Muslimani; Bram Grooten; Deepak Ranganatha Sastry Mamillapalli; Mykola Pechenizkiy; Decebal Constantin Mocanu; Matthew E. Taylor (AAMAS, 2025)

Sparse-to-Sparse Training of Diffusion Models. - Inês Cardoso Oliveira; Decebal Constantin Mocanu; Luis A. Leiva (Trans. Mach. Learn. Res., 2025)

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling. - Bram Grooten; Farid Hasanov; Chenxiang Zhang; Qiao Xiao; Boqian Wu; Zahra Atashgahi; Ghada Sokar; Shiwei Liu 003; Lu Yin 006; Elena Mocanu; Mykola Pechenizkiy; Decebal Constantin Mocanu (CoRR, 2025)

Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution. - Qiao Xiao; Alan Ansell; Boqian Wu; Lu Yin 006; Mykola Pechenizkiy; Shiwei Liu 003; Decebal Constantin Mocanu (CoRR, 2025)

Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity. - Qiao Xiao; Boqian Wu; Andrey Poddubnyy; Elena Mocanu; Phuong H. Nguyen; Mykola Pechenizkiy; Decebal Constantin Mocanu (CoRR, 2025)