6 citations · 24 across the 8 of their papers we have counts for
8 papers
AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
Petr Anokhin, Nikita Semenov, Artyom Sorokin +4
Advancements in the capabilities of Large Language Models (LLMs) have created a promising foundation for developing autonomous agents. With the right tools, these agents could lear…
Vote'n'Rank: Revision of Benchmarking with Social Choice Theory
Mark Rofin, Vladislav Mikhailov, Mikhail Florinskiy +5
The development of state-of-the-art systems in different applied areas of machine learning (ML) is driven by benchmarks, which have shaped the paradigm of evaluating generalisation…
An Optimal Transport Perspective on Unpaired Image Super-Resolution
Milena Gazdieva, Petr Mokrov, Litu Rout +4
Real-world image super-resolution (SR) tasks often do not have paired datasets, which limits the application of supervised techniques. As a result, the tasks are usually approached…
A Differentiable Language Model Adversarial Attack on Text Classifiers
Ivan Fursov, Alexey Zaytsev, Pavel Burnyshev +5
Robustness of huge Transformer-based models for natural language processing is an important issue due to their capabilities and wide adoption. One way to understand and improve rob…
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
I. Fursov, A. Zaytsev, N. Kluchnikov +2
An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models: minor changes of the input can force a model failure. Most of the state of…
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world
Ivan Fursov, Alexey Zaytsev, Nikita Kluchnikov +2
Deep learning models suffer from a phenomenon called adversarial attacks: we can apply minor changes to the model input to fool a classifier for a particular example. The literatur…