19 citations · 43 across the 8 of their papers we have counts for
12 papers
Fine-Grained Emotion Detection on GoEmotions: Experimental Comparison of Classical Machine Learning, BiLSTM, and Transformer Models
Ani Harutyunyan, Sachin Kumar
Fine-grained emotion recognition is a challenging multi-label NLP task due to label overlap and class imbalance. In this work, we benchmark three modeling families on the GoEmotion…
Plasticity as the Mirror of Empowerment
David Abel, Michael Bowling, André Barreto +13
Agents are minimally entities that are influenced by their past observations and act to influence future observations. This latter capacity is captured by empowerment, which has se…
Agency Is Frame-Dependent
David Abel, André Barreto, Michael Bowling +13
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determini…
Three Dogmas of Reinforcement Learning
David Abel, Mark K. Ho, Anna Harutyunyan
Modern reinforcement learning has been conditioned by at least three dogmas. The first is the environment spotlight, which refers to our tendency to focus on modeling environments…
Dynamical-System Model Predicts When Social Learners Impair Collective Performance
Vicky Chuqiao Yang, Mirta Galesic, Harvey McGuinness +1
A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the opti…
Useful Policy Invariant Shaping from Arbitrary Advice
Paniz Behboudian, Yash Satsangi, Matthew E. Taylor +2
Reinforcement learning is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in com…