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Found 15 papers, 4 papers with code
MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities
Events are fundamental building blocks of real-world happenings.
AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design
Structure-based drug design (SBDD), which aims to generate molecules that can bind tightly to the target protein, is an essential problem in drug discovery, and previous approaches have achieved initial success.
PE-GPT: A Physics-Informed Interactive Large Language Model for Power Converter Modulation Design
This paper proposes PE-GPT, a custom-tailored large language model uniquely adapted for power converter modulation design.
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges
In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection.
Say More with Less: Understanding Prompt Learning Behaviors through Gist Compression
It finetunes the compression plugin module and uses the representations of gist tokens to emulate the raw prompts in the vanilla language model.
MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
To facilitate the multi-modal retrieval tasks, we build the ClueWeb22-MM dataset based on the ClueWeb22 dataset, which regards anchor texts as queries, and exacts the related text and image documents from anchor-linked web pages.
Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution
Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations.
Feature-aware conditional GAN for category text generation
To address these issues, this paper proposes a novel GAN framework, the feature-aware conditional GAN (FA-GAN), for controllable category text generation.
Generative Adversarial Network
Multi-class Classification
+4
Particle swarm optimization with state-based adaptive velocity limit strategy
Several adaptive VL strategies have been introduced with which the performance of PSO can be improved.
Artificial-Intelligence-Based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter with Full ZVS Range and Optimal Efficiency
ZVS range and efficiency are two significant performance indicators for DAB converter.
Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter with Minimized Current Stress
However, to minimize the current stress when the DAB converter is under TPS modulation, two difficulties exist in analysis process and realization process, respectively.
Data-Driven Modeling with Experimental Augmentation for the Modulation Strategy of the Dual-Active-Bridge Converter
The D2EA approach is instantiated for the efficiency optimization of a hybrid modulation for neutral-point-clamped dual-active-bridge (NPC-DAB) converter.
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining.
Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation
Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal.
An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data
The rapid development in the field of System of Chip (SoC) technology, Internet of Things (IoT), cloud computing, and artificial intelligence has brought more possibilities of improving and solving the current problems.
