Ji Wang

Private Model Compression via Knowledge Distillation

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated …

Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models

2020 · arXiv (Cornell University)

Recent research has revealed that deep generative models including flow-based models and Variational Autoencoders may assign higher likelihoods to out-of-distribution (OOD) data than in-distribution (ID) data. However, we cannot sample OOD data from the model. …

Missing data reconstruction method based on Kmeans and GBDT combined model

2022 · International Conference on Electronic Information Technology (EIT 2022)

Complete and available data is of great significance for improving the theoretical line loss calculation in the low-voltage transformer area. However, with the upgrading of equipment, the electrical data presents the characteristics of small granularity …

AB-GRU: An attention-based bidirectional GRU model for multimodal sentiment fusion and analysis

2023 · Mathematical Biosciences & Engineering

Multimodal sentiment analysis is an important area of artificial intelligence. It integrates multiple modalities such as text, audio, video and image into a compact multimodal representation and obtains sentiment information from them. In this paper, …

Verification of message-passing uninterpreted programs

2024 · Science of Computer Programming

Message-passing programs involve several processes with channel-based communications to deal with tasks concurrently. The complex computations and communications between processes make the verification of message-passing programs hard. By regarding the functions in programs as uninterpreted …