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Found 25 papers, 11 papers with code
Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis
Extensive experiments show that training LRCD on real-world datasets can achieve commendable zero-shot performance across different target domains, and in some cases, it can even achieve competitive performance with some classic CDMs trained on the full response data on target domains.
A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments
To this end, this paper proposes a dual-fusion cognitive diagnosis framework (DFCD) to address the challenge of aligning two different modalities, i. e., textual semantic features and response-relevant features.
Preference Diffusion for Recommendation
Recommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data.
LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
Namely, the accuracy of most current LLM-based methods and the generality of optimization problem types that they can model are still limited.
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
To address these challenges, we formalize model merging as a multi-objective optimization problem and propose an automated optimization approach named MM-MO.
ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems
This paper contends that the oversmoothing issue arises from that existing CDMs seldom utilize response signals on exercises in the learning part but only use them as labels in the assessing part.
Leveraging Pedagogical Theories to Understand Student Learning Process with Graph-based Reasonable Knowledge Tracing
The advancement of deep learning in this field has led to deep-learning knowledge tracing (DLKT) models that prioritize high predictive accuracy.
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm, namely CDM-PSL, for expensive MOBO.
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications.
Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems
To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs.
Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems
The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function, and utilizes gradient-based optimization methods to effectively learn the student and exercise parameters.
On the Opportunities of Green Computing: A Survey
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
Scaling Multi-Objective Security Games Provably via Space Discretization Based Evolutionary Search
Then, a many-objective EA is used for optimization in the low-dimensional discrete solution space to obtain a well-spaced Pareto front.
Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype
Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations.
Differentiating Student Feedbacks for Knowledge Tracing
An accurate KT model can capture a student's mastery level of different knowledge topics, as reflected in their predicted performance on different questions.
Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games
This paper addresses policy learning in non-stationary environments and games with continuous actions.
BBTv2: Towards a Gradient-Free Future with Large Language Models
By contrast, gradient-free methods only require the forward computation of the PTM to tune the prompt, retaining the benefits of efficient tuning and deployment.
Black-Box Tuning for Language-Model-as-a-Service
In such a scenario, which we call Language-Model-as-a-Service (LMaaS), the gradients of PTMs are usually unavailable.
Revealing and controlling nuclear dynamics following inner-shell photoionization of N2
In this work, we apply the Monte Carlo wave packet method to study the ultrafast nuclear dynamics following inner-shell photoionization of N2 exposed to an ultrashort intense X-ray pulse.
Atomic Physics Atomic and Molecular Clusters
Derivative-Free Reinforcement Learning: A Review
In this article, we summarize methods of derivative-free reinforcement learning to date, and organize the methods in aspects including parameter updating, model selection, exploration, and parallel/distributed methods.
Exact power spectrum in a minimal hybrid model of stochastic gene expression oscillations
In our model, oscillations tend to occur when the protein is relatively stable and when gene switching is relatively slow.
ZOOpt: Toolbox for Derivative-Free Optimization
Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions.
Estimate exponential memory decay in Hidden Markov Model and its applications
Inference in hidden Markov model has been challenging in terms of scalability due to dependencies in the observation data.
Nonequilibrium and nonlinear kinetics as key determinants for bistability in fission yeast G2-M transition
A living cell is an open, nonequilibrium biochemical system where ATP hydrolysis serves as the energy source for a wide range of intracellular processes, possibly including the assurance for decision-making.
The Sampling-and-Learning Framework: A Statistical View of Evolutionary Algorithms
By summarizing a large range of EAs into the sampling-and-learning framework, we show that the framework directly admits a general analysis on the probable-absolute-approximate (PAA) query complexity.
