Publications d'Artem Zholus
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Artem Zholus
Domaines de recherche: Traitement du langage naturel, Apprentissage par renforcement
Activité
- Étudiant au doctorat: jan. 2023 - maintenant
Prépublications
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TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus, Carl Doersch, Yi Yang, Skanda Koppula, Viorica Patraucean, Xu Owen He, Ignacio Rocco, Mehdi S. M. Sajjadi, Sarath Chandar et Ross Goroshin
In ArXiv, 2025.
#Other
[website], [arXiv], [code], [YouTube] -
TRecViT: A Recurrent Video Transformer
Viorica Pătrăucean, Xu Owen He, Joseph Heyward, Chuhan Zhang, Mehdi S. M. Sajjadi, George-Cristian Muraru, Artem Zholus, Mahdi Karami, Ross Goroshin, Yutian Chen, Simon Osindero, João Carreira et Razvan Pascanu
In ArXiv, 2024.
#DL
[arXiv] -
Unraveling the Complexity of Memory in RL Agents: An Approach for Classification and Evaluation
Egor Cherepanov, Nikita Kachaev, Artem Zholus, Alexey K. Kovalev et Aleksandr I. Panov
In ArXiv, 2024.
#RL
[arXiv] -
Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent
Karolis Jucys, George Adamopoulos, Mehrab Hamidi, Stephanie Milani, Mohammad Reza Samsami, Artem Zholus, Sonia Joseph, Blake Richards, Irina Rish et Özgür Şimşek
Mechanistic Interpretability Workshop, ICML, 2024.
#Other
[arXiv]
Articles de conférence et de revue
2025
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IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents
Shrestha Mohanty, Negar Arabzadeh, Andrea Tupini, Yuxuan Sun, Alexey Skrynnik, Artem Zholus, Marc-Alexandre Côté et Julia Kiseleva
SIGIR, 2025.
#NLP
[arXiv] -
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Artem Zholus, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Daniil Polykovskiy, Sarath Chandar et Alex Zhavoronkov
AAAI Conference on Artificial Intelligence (AAAI), 2025. [Best poster award]
#DL, #RL
[arXiv], [website], [YouTube]
2024
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Mastering Memory Tasks with World Models
Mohammad Reza Samsami*, Artem Zholus*, Janarthanan Rajendran et Sarath Chandar
International Conference on Learning Representations (ICLR), 2024. [Oral presentation.]
#RL, #DL
[openreview], [arXiv]