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Found 16 papers, 0 papers with code
Enhancing Incremental Summarization with Structured Representations
Large language models (LLMs) often struggle with processing extensive input contexts, which can lead to redundant, inaccurate, or incoherent summaries.
From ChatGPT, DALL-E 3 to Sora: How has Generative AI Changed Digital Humanities Research and Services?
This article profoundly explores the application of large-scale language models in digital humanities research, revealing their significant potential in ancient book protection, intelligent processing, and academic innovation.
STRUM-LLM: Attributed and Structured Contrastive Summarization
Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages.
Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models
This paper explores the use of Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) models for nonlinear system identification and model-based control design.
A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units
Temperature control is a complex task due to its often unknown dynamics and disturbances.
An Augmentation Strategy for Visually Rich Documents
Many business workflows require extracting important fields from form-like documents (e. g. bank statements, bills of lading, purchase orders, etc.).
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models
A key bottleneck in building automatic extraction models for visually rich documents like invoices is the cost of acquiring the several thousand high-quality labeled documents that are needed to train a model with acceptable accuracy.
Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models
This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (NNARX) models.
Towards lifelong learning of Recurrent Neural Networks for control design
This paper proposes a method for lifelong learning of Recurrent Neural Networks, such as NNARX, ESN, LSTM, and GRU, to be used as plant models in control system synthesis.
An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models
This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks.
Data-Efficient Information Extraction from Form-Like Documents
Automating information extraction from form-like documents at scale is a pressing need due to its potential impact on automating business workflows across many industries like financial services, insurance, and healthcare.
On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments
This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks (RNN) in control design applications.
Personalized Heterogeneous Federated Learning with Gradient Similarity
In SPFL, the server uses the Softmax Normalized Gradient Similarity (SNGS) to weight the relationship between clients, and sends the personalized global model to each client.
PPKE: Knowledge Representation Learning by Path-based Pre-training
Entities may have complex interactions in a knowledge graph (KG), such as multi-step relationships, which can be viewed as graph contextual information of the entities.
Active Learning for Skewed Data Sets
Furthermore, there is usually only a small amount of initial training data available when building machine-learned models to solve such problems.
Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis
The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models.
