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Found 14 papers, 7 papers with code
Counterfactual Matters: Intrinsic Probing For Dialogue State Tracking
Our findings are: the performance variance of generative DSTs is not only due to the model structure itself, but can be attributed to the distribution of cross-domain values.
Triplet Contrastive Learning for Unsupervised Vehicle Re-identification
For the inter-class instance, a hybrid contrastive loss (HCL) re-defines the sample correlations by approaching the positive cluster features and leaving the all negative instance features.
BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization
To overcome this problem, we further develop a mixed expert block to extract semantic information for modeling the object boundaries of frames so that the semantic image prior can better guide the colorization process for better performance.
A Competitive Method for Dog Nose-print Re-identification
Vision-based pattern identification (such as face, fingerprint, iris etc.)
Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events
This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case.
SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation
Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe.
Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning
In this paper, we propose a meta-learning based semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation, denoted as MEDST.
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems
The state-of-the-art accuracy for DST is below 50{\%} for a multi-domain dialogue task.
Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts
In this paper, a methodology for the automatic prioritisation of suspicious file artefacts (i. e., file artefacts that are pertinent to the investigation) is proposed to reduce the manual analysis effort required.
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF.
Fast Matrix Factorization with Non-Uniform Weights on Missing Data
This poses an imbalanced learning problem, since the scale of missing entries is usually much larger than that of observed entries, but they cannot be ignored due to the valuable negative signal.
Adversarial Training Towards Robust Multimedia Recommender System
To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning.
Information Retrieval Multimedia
Adversarial Personalized Ranking for Recommendation
Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11. 2% on average and achieves state-of-the-art performance for item recommendation.
Outer Product-based Neural Collaborative Filtering
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.
