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Found 9 papers, 3 papers with code
Checkpoint Merging via Bayesian Optimization in LLM Pretraining
The rapid proliferation of large language models (LLMs) such as GPT-4 and Gemini underscores the intense demand for resources during their training processes, posing significant challenges due to substantial computational and environmental costs.
HeroNet: A Hybrid Retrieval-Generation Network for Conversational Bots
Specifically, the retrieval performance is improved while the model size is reduced by training two lightweight, task-specific adapter modules that share only one underlying T5-Encoder model.
Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network
In real-world applications, events are not always observable, and estimating event time is as important as predicting future events.
Multi-Scenario Bimetric-Balanced IoT Resource Allocation: An Evolutionary Approach
In this paper, we allocate IoT devices as resources for smart services with time-constrained resource requirements.
Requirements Elicitation in Cognitive Service for Recommendation
To this end, two phases must be applied: I. Sequence planning and Real-time detection of user requirement, II. Service resource selection and Response generation.
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation
The fundamental problem with these studies is that they ignore the evolution of services over time and the representation gap between services and requirements.
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism
However, relying only on aggregation to propagate information in dynamic graphs can result in delays in information propagation and thus affect the performance of the method.
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services
In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services.
LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition
In this paper, we first examine traditional active learning strategies in a specific case of BiLstm-CRF that has widely used in named entity recognition on several typical datasets.
