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Found 24 papers, 3 papers with code
Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics
These results highlight HTGNN's potential as a robust and accurate virtual sensing approach for complex systems, paving the way for improved monitoring, predictive maintenance, and enhanced system performance.
ComperDial: Commonsense Persona-grounded Dialogue Dataset and Benchmark
We propose a new benchmark, ComperDial, which facilitates the training and evaluation of evaluation metrics for open-domain dialogue systems.
Visual Echoes: A Simple Unified Transformer for Audio-Visual Generation
The recent audio-visual generation methods usually resort to huge large language model or composable diffusion models.
Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks
Since temperature and vibration signals exhibit vastly different dynamics, we propose Heterogeneous Temporal Graph Neural Networks (HTGNN), which explicitly models these signal types and their interactions for effective load prediction.
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing.
DiffuCOMET: Contextual Commonsense Knowledge Diffusion
Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models.
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain
To address both these issues, we introduce a natural language inference method for post-hoc adapting a trained persona extraction model to a new setting.
On the Language Encoder of Contrastive Cross-modal Models
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks.
Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association
In this paper, we focus on the wide existence of reporting bias in visual-language datasets, embodied as the object-attribute association, which can subsequentially degrade models trained on them.
Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems
The use of Internet of Things (IoT) sensors for air pollution monitoring has significantly increased, resulting in the deployment of low-cost sensors.
DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems
We rigorously evaluated DyEdgeGAT using both a synthetic dataset, simulating varying levels of fault severity, and a real-world industrial-scale multiphase flow facility benchmark with diverse fault types under varying operating conditions and detection complexities.
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Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction
Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features.
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
These structures are fundamental to understanding code.
This joke is [MASK]: Recognizing Humor and Offense with Prompting
Humor is a magnetic component in everyday human interactions and communications.
Modular and Parameter-Efficient Multimodal Fusion with Prompting
Recent research has made impressive progress in large-scale multimodal pre-training.
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework
We propose LMTurk, a novel approach that treats few-shot learners as crowdsourcing workers.
Discrete and Soft Prompting for Multilingual Models
It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs).
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.
Continual Learning for Natural Language Generation in Task-oriented Dialog Systems
Natural language generation (NLG) is an essential component of task-oriented dialog systems.
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models
We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning.
Quantifying the Contextualization of Word Representations with Semantic Class Probing
Pretrained language models have achieved a new state of the art on many NLP tasks, but there are still many open questions about how and why they work so well.
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction
We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world{'}s languages.
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable
A simple and effective context-based multilingual embedding learner is Levy et al. (2017)'s S-ID (sentence ID) method.
Embedding Learning Through Multilingual Concept Induction
We present a new method for estimating vector space representations of words: embedding learning by concept induction.
