Mixed Effect
Mixed effects modeling investigates how different factors influence an outcome, accounting for both fixed and random effects. Current research focuses on understanding the impact of various factors across diverse applications, employing diverse models such as deep neural networks, logistic regression, and random feature models, often within the context of interpretability and bias mitigation. This field is crucial for advancing understanding in various domains, from improving AI systems and human-computer interaction to enhancing medical diagnoses and optimizing industrial processes.
Papers
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model
Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn, Sungdong Kim, HyoungSeok Kim, Boseop Kim, Kyunghyun Cho, Gichang Lee, Woomyoung Park, Jung-Woo Ha, Nako Sung
The Effect of Preferences in Abstract Argumentation Under a Claim-Centric View
Michael Bernreiter, Wolfgang Dvorak, Anna Rapberger, Stefan Woltran
The effect of speech pathology on automatic speaker verification -- a large-scale study
Soroosh Tayebi Arasteh, Tobias Weise, Maria Schuster, Elmar Noeth, Andreas Maier, Seung Hee Yang
Single-grasp deformable object discrimination: the effect of gripper morphology, sensing modalities, and action parameters
Michal Pliska, Shubhan Patni, Michal Mares, Pavel Stoudek, Zdenek Straka, Karla Stepanova, Matej Hoffmann
Effects of Haptic Feedback on the Wrist during Virtual Manipulation
Mine Sarac, Allison M. Okamura, Massimiliano Di Luca
Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits
Aneta Neumann, Yue Xie, Frank Neumann
Perception of Mechanical Properties via Wrist Haptics: Effects of Feedback Congruence
Mine Sarac, Massimiliano di Luca, Allison M. Okamura