Computing
By Rosalind W. Picard. Quantum information & computation, 2021
1,541 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/c65fca016b205725340b0b2aa20db3fc6b9e1a35
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sven Buechel
- Udo Hahn
- Dorottya Demszky
- Cornelia Sindermann
- Steven J. Lovegrove
- Dana Movshovitz-Attias
- Curtis G. Northcutt
- Yan Wang
Citations in the corpus
- Emotion Analysis as a Regression Problem - Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation (2016, background): “From a computational perspective, AI researchers have already started investigating into this field [26], since progress in this area will pave the way to even smarter and more natural computational agents for human-computer interaction, such as avatars or robots .”
- Emotion Research by the People, for the People (2010, background): “However, the biggest peak measured was not during that climax, but was actually much earlier, at a point where, as it happened, the game controls stopped working (Picard, 1997).”
- GoEmotions: A Dataset of Fine-Grained Emotions (2020, background): “With as many as a handful of words we are able to express a wide variety of subtle and complex emotions, and it has thus been a long-term goal to enable machines to understand affect and emotion (Picard, 1997).”
- A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “Since the concept of affective computing [2] was proposed by Prof.”
- EgoCom: A Multi-Person Multi-Modal Egocentric Communications Dataset (2020, background): “Multi-modal data can be useful for tasks like multi-party speech recognition and predicting turn-taking by combining granularity of verbal and non-verbal cues (Stratou and Morency, 2017; Picard, 2000).”
- Assessing the Attitude Towards Artificial Intelligence: Introduction of a Short Measure in German, Chinese, and English Language (2020, methodology): “All analyses were implemented using the statistical software R version 3.5.2 [21], R-Studio version 1.1.463 [22], and several R-packages such as car [23], dplyr [24], effsize [25], gmodels [26], GPArotation [27], Hmisc [28], lavaan [29], ppcor [30], psych [31], and semTools [32].”