Search Results for author:
Found 13 papers, 6 papers with code
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis
Emotion Recognition in Conversation (ERC) has attracted increasing attention in the affective computing research field.
DOF: Accelerating High-order Differential Operators with Forward Propagation
Solving partial differential equations (PDEs) efficiently is essential for analyzing complex physical systems.
Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data
This kind of continuous monitoring, in contrast to the short-term one, collects an extended period of fetal heart data.
Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm
Landslide is a natural disaster that can easily threaten local ecology, people's lives and property.
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry.
AlignScore: Evaluating Factual Consistency with a Unified Alignment Function
AlignScore is based on a general function of information alignment between two arbitrary text pieces.
Dataset for predicting cybersickness from a virtual navigation task
This work presents a dataset collected to predict cybersickness in virtual reality environments.
Reconstruction Task Finds Universal Winning Tickets
Pruning well-trained neural networks is effective to achieve a promising accuracy-efficiency trade-off in computer vision regimes.
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition
Multimodal emotion recognition study is hindered by the lack of labelled corpora in terms of scale and diversity, due to the high annotation cost and label ambiguity.
Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities
However, in real-world applications, we often encounter the problem of missing modality, and which modalities will be missing is uncertain.
Towards a Theoretical Framework of Out-of-Distribution Generalization
We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement Learning
Since trick-taking game requires high level of not only reasoning, but also inference to excel, it can be a new milestone for imperfect information game AI.
