19 citations · 20 across the 2 of their papers we have counts for
3 papers
cs.AI2024★ 1 cited
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Guoli Yin, Haoping Bai, Shuang Ma +21
Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, whi…
cs.CV2019
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
Jörg Wagner, Jan Mathias Köhler, Tobias Gindele +3
To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a pos…
cs.AI2016★ 19 cited
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Michael Herman, Tobias Gindele, Jörg Wagner +2
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the a…