Gosztolya G, Grósz T, Tóth L, Markó A, Csapó TGábor.  2020.  Applying DNN Adaptation to Reduce the Session Dependency of Ultrasound Tongue Imaging-based Silent Speech Interfaces. Acta Polytechnica Hungarica. 17:109–124.

Hegedűs I, Danner G, Jelasity M.  2020.  Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning. Machine Learning and Knowledge Discovery in Databases. :317–332.

Ferenc R, Viszkok T, Aladics T, Jász J, Hegedűs P.  2020.  Deep-water framework: The Swiss army knife of humans working with machine learning models. SoftwareX. 12:100551.

Berend G.  2020.  Efficient algorithm to compute Markov transitional probabilities for a desired PageRank. EPJ Data Science. 9:23.

Antal G, Keleti M, Hegedűs P.  2020.  Exploring the Security Awareness of the Python and JavaScript Open Source Communities. Proceedings of the 17th International Conference on Mining Software Repositories (MSR). :16–20.

Berend G.  2020.  Massively Multilingual Sparse Word Representations. Eighth International Conference on Learning Representations - ICLR 2020. . :1-16.

Tóth L, Nagy B, Vidács L, Gyimóthy T.  2020.  Mining Hypernyms Semantic Relations from Stack Overflow. Proceedings of the First International Workshop on Knowledge Graph for Software Engineering, KG4SE 2020 - ICSEW.

Kmetty Z, Vincze V, Demszky D, Ring O, Nagy B, Szabó MKatalin.  2020.  Pártélet: A Hungarian Corpus of Propaganda Texts from the Hungarian Socialist Era. Proceedings of The 12th Language Resources and Evaluation Conference. :2374-2381.

Berend G, Kis-Szabó N, Szántó Z.  2020.  ProsperAMnet at the FinSim Task: Detecting hypernyms of financial concepts via measuring the information stored in sparse word representations. Second Workshop on Financial Technology and Natural Language Processing. :98-103.

Gosztolya G, Grósz T, Tóth L.  2020.  Social Signal Detection by Probabilistic Sampling DNN Training. IEEE Transactions on Affective Computing. 10:164–177.

Kicsi A, Vidács L, Gyimothy T.  2020.  TestRoutes: A Manually Curated Method Level Dataset for Test-to-Code Traceability. Proceedings of the 17th International Conference on Mining Software Repositories, MSR 2020. :593-597.

Balogh V, Berend G, Diochnos DI, Turán G.  2020.  Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). :7399-7406.

Gosztolya G.  2020.  Using the Fisher Vector Representation for Audio-based Emotion Recognition. Acta Polytechnica Hungarica. 17:7–23.

Csuvik V, Horváth D, Horváth F, Vidács L.  2020.  Utilizing Source Code Embeddings to Identify Correct Patches. Proceedings of the Second International Workshop on Intelligent Bug Fixing (IBF 2020). :18-25.

Tóth L, Nagy B, Gyimóthy T, Vidács L.  2020.  Why Will My Question Be Closed? NLP-Based Pre-Submission Predictions of Question Closing Reasons on Stack Overflow Proceedings of the 42nd International Conference on Software Engineering, NIER Track (ICSE 2020). :105-108.

López JVicente Eg, Orozco-Arroyave JRafael, Gosztolya G.  2019.  Assessing Parkinson's Disease From Speech by Using Fisher Vectors. Proceedings of Interspeech. :3063–3067.

Gosztolya G, Pintér Á, Tóth L, Grósz T, Markó A, Csapó TGábor.  2019.  Autoencoder-Based Articulatory-to-Acoustic Mapping for Ultrasound Silent Speech Interfaces. Proceedings of IJCNN.

Gosztolya G, Busa-Fekete R.  2019.  Calibrating AdaBoost for Phoneme Classification. Soft Computing. 23:115–128.

Gosztolya G, Tóth L.  2019.  Calibrating DNN Posterior Probability Estimates of HMM/DNN Models to Improve Social Signal Detection From Audio Data. Proceedings of Interspeech. :515–519.

Ferenc R, Hegedűs P, Gyimesi P, Antal G, Bán D, Gyimóthy T.  2019.  Challenging Machine Learning Algorithms in Predicting Vulnerable JavaScript Functions. Proceedings of the 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering. :8–14.

Horvath F, Gergely T, Beszedes A, Tengeri D, Balogh G, Gyimothy T.  2019.  Code coverage differences of Java bytecode and source code instrumentation tools. SOFTWARE QUALITY JOURNAL. 27:79-123.

Gergely T, Balogh G, Horvath F, Vancsics B, Beszedes A, Gyimothy T.  2019.  Differences between a static and a dynamic test-to-code traceability recovery method. SOFTWARE QUALITY JOURNAL. 27:797-822.

Csuvik V, Kicsi A, Vidács L.  2019.  Evaluation of Textual Similarity Techniques in Code Level Traceability. Proceedings of the 19th International Conference on Computational Science and Its Applications (ICCSA 2019). :529-543.

Kicsi A, Rákóczi M, Vidács L.  2019.  Exploration and Mining of Source Code Level Traceability Links on Stack Overflow. Proceedings of ICSOFT 2019, 14th International Conference on Software Technologies. :339-346.

Kicsi A, Csuvik V, Vidács L, Horváth F, Beszédes Á, Gyimóthy T, Kocsis F.  2019.  Feature Analysis using Information Retrieval, Community Detection and Structural Analysis Methods in Product Line Adoption. Journal of Systems and Software. 155:70-90.