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Found 7 papers, 2 papers with code
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability.
DLFormer: Enhancing Explainability in Multivariate Time Series Forecasting using Distributed Lag Embedding
Although attention-based models have limitations in representing the individual influences of each variable, these models can influence the temporal dependencies in time series prediction and the magnitude of the influence of individual variables.
Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task
By leveraging the power of language models to capture semantic representations, LKT effectively incorporates textual information and significantly outperforms previous KT models on large benchmark datasets.
Deep Neural Network NMPC for Computationally Tractable Optimal Power Management of Hybrid Electric Vehicle
This study presents a method for deep neural network nonlinear model predictive control (DNN-MPC) to reduce computational complexity, and we show its practical utility through its application in optimizing the energy management of hybrid electric vehicles (HEVs).
NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency
We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data.
Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data
However, because this approach is learned in an independent model for each component, it cannot learn the relationships between time-series components.
CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics
This paper presents CoVA, a novel cascade architecture that splits the cascade computation between compressed domain and pixel domain to address the decoding bottleneck, supporting both temporal and spatial queries.
