17 research outputs found
Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis
Video sentiment analysis as a decision-making process is inherently complex,
involving the fusion of decisions from multiple modalities and the so-caused
cognitive biases. Inspired by recent advances in quantum cognition, we show
that the sentiment judgment from one modality could be incompatible with the
judgment from another, i.e., the order matters and they cannot be jointly
measured to produce a final decision. Thus the cognitive process exhibits
"quantum-like" biases that cannot be captured by classical probability
theories. Accordingly, we propose a fundamentally new, quantum cognitively
motivated fusion strategy for predicting sentiment judgments. In particular, we
formulate utterances as quantum superposition states of positive and negative
sentiment judgments, and uni-modal classifiers as mutually incompatible
observables, on a complex-valued Hilbert space with positive-operator valued
measures. Experiments on two benchmarking datasets illustrate that our model
significantly outperforms various existing decision level and a range of
state-of-the-art content-level fusion approaches. The results also show that
the concept of incompatibility allows effective handling of all combination
patterns, including those extreme cases that are wrongly predicted by all
uni-modal classifiers.Comment: The uploaded version is a preprint of the accepted AAAI-21 pape
On the Generalization of the C-Bound to Structured Output Ensemble Methods
This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic version of the \Cbound, an upper bound over the risk of models expressed as a weighted majority vote that is based on the first and second statistical moments of the vote's margin. This bound may advantageously be applied on more complex outputs such as multiclass labels and multilabel, and allow to consider margin relaxations. These results open the way to develop new ensemble methods for structured output prediction with PAC-Bayesian guarantees
A Hierarchical Attention-based Contrastive Learning Method for Micro Video Popularity Prediction
Micro videos popularity prediction (MVPP) has recently attracted widespread research interests given the increasing prevalence of video-based social platforms. However, previous studies have overlooked the unique patterns between popular and unpopular videos and the interactions between asynchronous features different data dimensions. To address this, we propose a novel hierarchical attention contrastive learning method named HACL, which extracts explainable representation features, learns their asynchronous interactions from both temporal and spatial levels, and separates the positive and negative embeddings identities. This reveals video popularity in a contrastive and interrelated view, and thus can be responsible for a better MVPP. Dual neural networks account for separate positive and negative patterns via contrastive learning. To obtain the temporal-wise interaction coefficients, we propose a Hadamard-product based attention approach to optimize the trainable attention-map matrices. Results from our experiments on a TikTok micro video dataset show that HACL outperforms benchmarks and provides insightful managerial implications