Search Results for author:
Found 27 papers, 5 papers with code
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base.
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Robust Pose Transfer with Dynamic Details using Neural Video Rendering
Moreover, we introduce a concise temporal loss in the training stage to suppress the detail flickering that is made more visible due to high-quality dynamic details generated by our method.
NNCFR: Minimize Counterfactual Regret with Neural Networks
Towards this problem, a recent method, \textit{Deep CFR} alleviates the need for abstraction and expert knowledge by applying deep neural networks directly to CFR in full games.
Adaptive Compact Attention For Few-shot Video-to-video Translation
This paper proposes an adaptive compact attention model for few-shot video-to-video translation.
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection
To minimize the reconstruction error of normal data and maximize this of anomaly data, we do not just ensure normal data to reconstruct well, but also try to make the reconstruction of anomaly data consistent with the distribution of normal data, then anomalies will get higher reconstruction errors.
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.
Task-agnostic Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Review of Learning-Assisted Power System Optimization
With dramatic breakthroughs in recent years, machine learning is showing great potential to upgrade the toolbox for power system optimization.
Systems and Control Systems and Control Optimization and Control
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences
EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation.
Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning
Extracting effective deep features to represent content and style information is the key to universal style transfer.
Noise Robust Named Entity Understanding for Voice Assistants
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries.
CPOT: Channel Pruning via Optimal Transport
Recent advances in deep neural networks (DNNs) lead to tremendously growing network parameters, making the deployments of DNNs on platforms with limited resources extremely difficult.
Minimally Supervised Categorization of Text with Metadata
Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity.
Automatic Textual Evidence Mining in COVID-19 Literature
We created this EVIDENCEMINER system for automatic textual evidence mining in COVID-19 literature.
Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
We created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13).
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
With the massive number of repositories available, there is a pressing need for topic-based search.
Variational Autoregressive Decoder for Neural Response Generation
Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation.
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.
