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2020 – today
- 2024
[i16]Théophile Champion, Howard Bowman, Dimitrije Markovic, Marek Grzes:
Reframing the Expected Free Energy: Four Formulations and a Unification. CoRR abs/2402.14460 (2024)- 2023
- [j13]Lisa Bonheme, Marek Grzes:
Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders. J. Mach. Learn. Res. 24: 324:1-324:30 (2023) - [c38]Piotr Sawicki, Marek Grzes, Fabrício Góes, Anna Jordanous, Dan Brown, Simona Paraskevopoulou, Max Peeperkorn, Aisha Khatun:
On the power of special-purpose GPT models to create and evaluate new poetry in old styles. ICCC 2023: 10-19 - [c37]Piotr Sawicki, Marek Grzes, Fabrício Góes, Dan Brown, Max Peeperkorn, Aisha Khatun:
Bits of Grass: Does GPT already know how to write like Whitman? ICCC 2023: 317-321 - [c36]Fabrício Góes, Piotr Sawicki, Marek Grzes, Marco Volpe, Jacob Watson:
Pushing GPT's Creativity to Its Limits: Alternative Uses and Torrance Tests. ICCC 2023: 342-346 - [c35]Fabrício Góes, Piotr Sawicki, Marek Grzes, Marco Volpe, Dan Brown:
Is GPT-4 Good Enough to Evaluate Jokes? ICCC 2023: 367-371 - [c34]Lisa Bonheme, Marek Grzes:
The Polarised Regime of identifiable Variational Autoencoders. Tiny Papers @ ICLR 2023 - [c33]Peter Clapham, Marek Grzes:
Posterior Collapse in Variational Gradient Origin Networks. ICMLA 2023: 980-987 - [i15]Théophile Champion, Marek Grzes, Lisa Bonheme, Howard Bowman:
Deconstructing deep active inference. CoRR abs/2303.01618 (2023) - [i14]Lisa Bonheme, Marek Grzes:
How good are variational autoencoders at transfer learning? CoRR abs/2304.10767 (2023) - [i13]Piotr Sawicki, Marek Grzes, Fabrício Góes, Dan Brown, Max Peeperkorn, Aisha Khatun:
Bits of Grass: Does GPT already know how to write like Whitman? CoRR abs/2305.11064 (2023) - 2022
- [j12]Théophile Champion, Marek Grzes, Howard Bowman:
Branching Time Active Inference with Bayesian Filtering. Neural Comput. 34(10): 2132-2144 (2022) - [j11]Théophile Champion
, Lancelot Da Costa, Howard Bowman, Marek Grzes:
Branching Time Active Inference: The theory and its generality. Neural Networks 151: 295-316 (2022) - [j10]Théophile Champion, Howard Bowman, Marek Grzes:
Branching time active inference: Empirical study and complexity class analysis. Neural Networks 152: 450-466 (2022) - [c32]Piotr Sawicki, Marek Grzes, Anna Jordanous, Dan Brown, Max Peeperkorn:
Training GPT-2 to represent two Romantic-era authors: challenges, evaluations and pitfalls. ICCC 2022: 34-43 - [i12]Lisa Bonheme, Marek Grzes:
How do Variational Autoencoders Learn? Insights from Representational Similarity. CoRR abs/2205.08399 (2022) - [i11]Théophile Champion, Marek Grzes, Howard Bowman:
Multi-Modal and Multi-Factor Branching Time Active Inference. CoRR abs/2206.12503 (2022) - [i10]Lisa Bonheme, Marek Grzes:
FONDUE: an algorithm to find the optimal dimensionality of the latent representations of variational autoencoders. CoRR abs/2209.12806 (2022) - [i9]Fabrício Góes, Zisen Zhou, Piotr Sawicki, Marek Grzes, Daniel G. Brown:
Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as Judges. CoRR abs/2212.11214 (2022) - 2021
- [j9]Théophile Champion, Marek Grzes, Howard Bowman:
Realizing Active Inference in Variational Message Passing: The Outcome-Blind Certainty Seeker. Neural Comput. 33(10): 2762-2826 (2021) - [i8]Théophile Champion, Marek Grzes, Howard Bowman:
Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker. CoRR abs/2104.11798 (2021) - [i7]Lisa Bonheme, Marek Grzes:
Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders. CoRR abs/2109.12679 (2021) - [i6]Théophile Champion, Lancelot Da Costa, Howard Bowman, Marek Grzes:
Branching Time Active Inference: the theory and its generality. CoRR abs/2111.11107 (2021) - [i5]Théophile Champion, Howard Bowman, Marek Grzes:
Branching Time Active Inference: empirical study and complexity class analysis. CoRR abs/2111.11276 (2021) - [i4]Théophile Champion, Marek Grzes, Howard Bowman:
Branching Time Active Inference with Bayesian Filtering. CoRR abs/2112.07406 (2021) - 2020
- [c31]Lisa Bonheme, Marek Grzes:
SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes. SemEval@COLING 2020: 804-816
2010 – 2019
- 2019
- [c30]Rogério de Lemos, Marek Grzes:
Self-adaptive artificial intelligence. SEAMS@ICSE 2019: 155-156 - [c29]Lee Harris, Marek Grzes:
Comparing Explanations between Random Forests and Artificial Neural Networks. SMC 2019: 2978-2985 - 2018
- [c28]Farhana Ferdousi Liza, Marek Grzes:
Improving Language Modelling with Noise Contrastive Estimation. AAAI 2018: 5277-5284 - [i3]Jack Shannon, Marek Grzes:
Reinforcement Learning using Augmented Neural Networks. CoRR abs/1806.07692 (2018) - 2017
- [c27]Marek Grzes:
Reward Shaping in Episodic Reinforcement Learning. AAMAS 2017: 565-573 - [i2]Farhana Ferdousi Liza, Marek Grzes:
Improving Language Modelling with Noise-contrastive estimation. CoRR abs/1709.07758 (2017) - 2016
- [c26]Farhana Ferdousi Liza, Marek Grzes:
Estimating the Accuracy of Spectral Learning for HMMs. AIMSA 2016: 46-56 - [c25]Farhana Ferdousi Liza, Marek Grzes:
A Spectral Method that Worked Well in the SPiCe'16 Competition. ICGI 2016: 143-148 - [c24]Farhana Ferdousi Liza, Marek Grzes:
An Improved Crowdsourcing Based Evaluation Technique for Word Embedding Methods. RepEval@ACL 2016: 55-61 - 2015
- [j8]Mauro Vallati, Lukás Chrpa, Marek Grzes, Thomas Leo McCluskey, Mark Roberts, Scott Sanner:
The 2014 International Planning Competition: Progress and Trends. AI Mag. 36(3): 90-98 (2015) - [j7]Marek Grzes, Pascal Poupart, Xiao Yang, Jesse Hoey:
Energy Efficient Execution of POMDP Policies. IEEE Trans. Cybern. 45(11): 2484-2497 (2015) - [c23]Marek Grzes, Pascal Poupart:
Incremental Policy Iteration with Guaranteed Escape from Local Optima in POMDP Planning. AAMAS 2015: 1249-1257 - 2014
- [j6]Marcin Czajkowski, Marek Grzes, Marek Kretowski:
Multi-test decision tree and its application to microarray data classification. Artif. Intell. Medicine 61(1): 35-44 (2014) - [j5]Marek Grzes, Jesse Hoey, Shehroz S. Khan, Alex Mihailidis, Stephen Czarnuch, Daniel Jackson, Andrew Monk:
Relational approach to knowledge engineering for POMDP-based assistance systems as a translation of a psychological model. Int. J. Approx. Reason. 55(1): 36-58 (2014) - [c22]Marek Grzes, Pascal Poupart:
POMDP planning and execution in an augmented space. AAMAS 2014: 757-764 - 2013
- [c21]Marek Grzes, Pascal Poupart, Jesse Hoey:
Controller Compilation and Compression for Resource Constrained Applications. ADT 2013: 193-207 - [c20]Marek Grzes, Pascal Poupart, Jesse Hoey:
Isomorph-Free Branch and Bound Search for Finite State Controllers. IJCAI 2013: 2282-2290 - [c19]Marek Grzes, Jesse Hoey:
On the convergence of techniques that improve value iteration. IJCNN 2013: 1-8 - 2012
- [c18]Marek Grzes, Jesse Hoey:
Analysis of methods for solving MDPs. AAMAS 2012: 1237-1238 - [e1]Peter Vrancx, Matthew Knudson, Marek Grzes:
Adaptive and Learning Agents - International Workshop, ALA 2011, Held at AAMAS 2011, Taipei, Taiwan, May 2, 2011, Revised Selected Papers. Lecture Notes in Computer Science 7113, Springer 2012, ISBN 978-3-642-28498-4 [contents] - [i1]Marek Grzes, Jesse Hoey, Shehroz S. Khan, Alex Mihailidis, Stephen Czarnuch, Daniel Jackson, Andrew Monk:
Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model. CoRR abs/1206.5698 (2012) - 2011
- [j4]Sam Devlin, Daniel Kudenko, Marek Grzes:
An Empirical Study of Potential-Based Reward Shaping and Advice in Complex, Multi-Agent Systems. Adv. Complex Syst. 14(2): 251-278 (2011) - [c17]Jesse Hoey, Marek Grzes:
Distributed Control of Situated Assistance in Large Domains with Many Tasks. ICAPS 2011 - [c16]Marek Grzes, Jesse Hoey:
Efficient planning in R-max. AAMAS 2011: 963-970 - [c15]Sam Devlin, Marek Grzes, Daniel Kudenko:
Multi-agent, reward shaping for RoboCup KeepAway. AAMAS 2011: 1227-1228 - [c14]Marcin Czajkowski, Marek Grzes, Marek Kretowski:
Multi-Test Decision Trees for Gene Expression Data Analysis. SIIS 2011: 154-167 - 2010
- [b1]Marek Grzes:
Improving exploration in reinforcement learning through domain knowledge and parameter analysis. University of York, UK, 2010 - [j3]Marek Grzes, Daniel Kudenko:
Online learning of shaping rewards in reinforcement learning. Neural Networks 23(4): 541-550 (2010) - [c13]Marek Grzes, Daniel Kudenko:
PAC-MDP learning with knowledge-based admissible models. AAMAS 2010: 349-358
2000 – 2009
- 2009
- [j2]Marek Grzes, Daniel Kudenko:
Reinforcement Learning with Reward Shaping and Mixed Resolution Function Approximation. Int. J. Agent Technol. Syst. 1(2): 36-54 (2009) - [c12]Daniel Kudenko, Marek Grzes:
Knowledge-Based Reinforcement Learning for Data Mining. ADMI 2009: 21-22 - [c11]Sam Devlin, Marek Grzes, Daniel Kudenko:
Reinforcement Learning in RoboCup KeepAway with Partial Observability. IAT 2009: 201-208 - [c10]Marek Grzes, Daniel Kudenko:
Improving Optimistic Exploration in Model-Free Reinforcement Learning. ICANNGA 2009: 360-369 - [c9]Marek Grzes, Daniel Kudenko:
Theoretical and Empirical Analysis of Reward Shaping in Reinforcement Learning. ICMLA 2009: 337-344 - 2008
- [c8]Marek Grzes, Daniel Kudenko:
Robustness Analysis of SARSA(lambda): Different Models of Reward and Initialisation. AIMSA 2008: 144-156 - [c7]Marek Grzes, Daniel Kudenko:
An Empirical Analysis of the Impact of Prioritised Sweeping on the DynaQ's Performance. ICAISC 2008: 1041-1051 - [c6]Marek Grzes, Daniel Kudenko:
Multigrid Reinforcement Learning with Reward Shaping. ICANN (1) 2008: 357-366 - 2007
- [j1]Marek Kretowski, Marek Grzes:
Evolutionary Induction of Mixed Decision Trees. Int. J. Data Warehous. Min. 3(4): 68-82 (2007) - [c5]Marek Kretowski, Marek Grzes:
Evolutionary Induction of Decision Trees for Misclassification Cost Minimization. ICANNGA (1) 2007: 1-10 - 2006
- [c4]Marek Kretowski, Marek Grzes:
Mixed Decision Trees: An Evolutionary Approach. DaWaK 2006: 260-269 - [c3]Marek Kretowski, Marek Grzes:
Evolutionary Learning of Linear Trees with Embedded Feature Selection. ICAISC 2006: 400-409 - [c2]Marek Kretowski, Marek Grzes:
Evolutionary Induction of Cost-Sensitive Decision Trees. ISMIS 2006: 121-126 - 2005
- [c1]Marek Kretowski, Marek Grzes:
Global Induction of Oblique Decision Trees: An Evolutionary Approach. Intelligent Information Systems 2005: 309-318 - [p1]Marek Kretowski, Marek Grzes:
Global learning of decision trees by an evolutionary algorithm. Information Processing and Security Systems 2005: 401-410
Coauthor Index
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last updated on 2024-10-22 21:18 CEST by the dblp team
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