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Found 13 papers, 6 papers with code
CodeS: Natural Language to Code Repository via Multi-Layer Sketch
For feedback-based evaluation, we develop a VSCode plugin for CodeS and engage 30 participants in conducting empirical studies.
Improving Natural Language Capability of Code Large Language Model
Code large language models (Code LLMs) have demonstrated remarkable performance in code generation.
A GAN-based data poisoning framework against anomaly detection in vertical federated learning
Specifically, the malicious participant initially employs semi-supervised learning to train a surrogate target model.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Can Programming Languages Boost Each Other via Instruction Tuning?
Results demonstrate that programming languages can significantly improve each other.
Multi-stage Neural Networks: Function Approximator of Machine Precision
We demonstrate that the prediction error from the multi-stage training for both regression problems and physics-informed neural networks can nearly reach the machine-precision $O(10^{-16})$ of double-floating point within a finite number of iterations.
Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation
Specifically, for avoiding the exponential expansion of neighbors, we propose a hierarchical message aggregation mechanism to interact separately with low-order neighbors and meta-path-constrained high-order neighbors.
Prescribed Time Time-varying Output Formation Tracking for Uncertain Heterogeneous Multi-agent Systems
The time-varying output formation tracking for the heterogeneous multi-agent systems (MAS) is investigated in this paper.
Large Language Models Meet NL2Code: A Survey
The task of generating code from a natural language description, or NL2Code, is considered a pressing and significant challenge in code intelligence.
When Language Model Meets Private Library
In this paper, we investigate how to equip pre-trained language models with the ability of code generation for private libraries.
CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation
Usually, expensive text-code paired data is essential for training a code generation model.
Ranked #121 on
Code Generation
on HumanEval
Asymptotic self-similar blow-up profile for three-dimensional axisymmetric Euler equations using neural networks
Whether there exist finite time blow-up solutions for the 2-D Boussinesq and the 3-D Euler equations are of fundamental importance to the field of fluid mechanics.
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
With this key observation, we protect data privacy and allow the disclosure of feature meaning by concealing decision paths and adapt a communication-efficient secure computation method for inference outputs.
