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Found 21 papers, 5 papers with code
Is AI Robust Enough for Scientific Research?
We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs.
LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG
The increasing use of smart devices has emphasized the critical role of maintenance in production activities.
DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation
DTN introduces multiple diversified task-specific feature interaction methods and task-sensitive network in MTL networks, enabling the model to learn task-specific diversified feature interaction representations, which improves the efficiency of joint representation learning in a general setup.
An Outline of Prognostics and Health Management Large Model: Concepts, Paradigms, and Challenges
To this end, based on a systematic analysis of the current challenges and bottlenecks in PHM, as well as the research status and advantages of Large Model, we propose a novel concept and three progressive paradigms of Prognosis and Health Management Large Model (PHM-LM) through the integration of the Large Model with PHM.
DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning
While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in translating natural language questions into Structured Query Language (NL2SQL), the question of how to select the most beneficial demonstration examples remains an open research problem.
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
In practical wireless systems, the communication links among nodes are usually unreliable due to wireless fading and receiver noise, consequently resulting in performance degradation of GNNs.
SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder
Furthermore, compared to these competitive models, our proposed encoder enhances the downstream performance of NL2SQL models in 1-shot in-context learning scenarios by 1-2\% for GPT-3. 5-turbo, 4-8\% for CodeLlama-7B, and 2-3\% for CodeLlama-13B.
Effective Quantization for Diffusion Models on CPUs
Diffusion models have gained popularity for generating images from textual descriptions.
TSONN: Time-stepping-oriented neural network for solving partial differential equations
Deep neural networks (DNNs), especially physics-informed neural networks (PINNs), have recently become a new popular method for solving forward and inverse problems governed by partial differential equations (PDEs).
LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics
The COVID-19 pandemic has accentuated socioeconomic disparities across various racial and ethnic groups in the United States.
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
Large Language Models (LLMs) have demonstrated exceptional proficiency in language-related tasks, but their deployment poses significant challenges due to substantial memory and storage requirements.
Social media use among American Indians in South Dakota: Preferences and perceptions
Most of the participants reported that the use of social media increased tremendously during COVID-19 and had perceptions of more negative effects than positive effects.
Bootstrap The Original Latent: Learning a Private Model from a Black-box Model
In this paper, considering the balance of data/model privacy of model owners and user needs, we propose a new setting called Back-Propagated Black-Box Adaptation (BPBA) for users to better train their private models via the guidance of the back-propagated results of a Black-box foundation/source model.
Testability-Aware Low Power Controller Design with Evolutionary Learning
The proposed GA-guided XORNets also allows reducing the number of control bits, and the total testing time decreases by 20. 78% on average and up to 47. 09% compared to the existing design without sacrificing test coverage.
Image Magnification Network for Vessel Segmentation in OCTA Images
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality that allows micron-level resolution to visualize the retinal microvasculature.
UCNN: A Convolutional Strategy on Unstructured Mesh
In order to overcome the limitations of FNN and CNN, the unstructured convolutional neural network (UCNN) is proposed, which aggregates and effectively exploits the features of neighbour nodes through the weight function.
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling
We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling.
Machine Learning of Partial Differential Equations from Noise Data
Machine learning of partial differential equations from data is a potential breakthrough to solve the lack of physical equations in complex dynamic systems, but because numerical differentiation is ill-posed to noise data, noise has become the biggest obstacle in the application of partial differential equation identification method.
Layer Pruning for Accelerating Very Deep Neural Networks
In this paper, we propose an adaptive pruning method.
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Our method is built as an end-to-end framework for segmentation and classification.
FD-FCN: 3D Fully Dense and Fully Convolutional Network for Semantic Segmentation of Brain Anatomy
Developed from the seminal FCN with an end-to-end learning-based approach and constructed by newly designed dense blocks including a dense fully-connected layer, the proposed FD-FCN is different from other FCN-based methods and leads to an outperformance in the perspective of both efficiency and accuracy.
