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Found 15 papers, 6 papers with code
SelfCodeAlign: Self-Alignment for Code Generation
In our primary experiments, we use SelfCodeAlign with CodeQwen1. 5-7B to generate a dataset of 74k instruction-response pairs.
Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning
Moreover, model performance on FIM tasks deteriorates significantly without these unrealistic assumptions.
Evaluating Language Models for Efficient Code Generation
We introduce Differential Performance Evaluation (DPE), a framework designed to reliably evaluate Large Language Models (LLMs) for efficient code generation.
RepoQA: Evaluating Long Context Code Understanding
Recent advances have been improving the context windows of Large Language Models (LLMs).
XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts
We introduce XFT, a simple yet powerful training scheme, by simply merging upcycled Mixture-of-Experts (MoE) to unleash the performance limit of instruction-tuned code Large Language Models (LLMs).
Magicoder: Empowering Code Generation with OSS-Instruct
Magicoder models are trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets to generate diverse instruction data for code.
Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer
Effective classification of autonomous vehicle (AV) driving behavior emerges as a critical area for diagnosing AV operation faults, enhancing autonomous driving algorithms, and reducing accident rates.
Revisiting the Plastic Surgery Hypothesis via Large Language Models
Traditional APR tools have largely leveraged the plastic surgery hypothesis by designing manual or heuristic-based approaches to exploit such existing code ingredients.
Establishing a stronger baseline for lightweight contrastive models
A common practice to address this problem is to introduce a pretrained contrastive teacher model and train the lightweight networks with distillation signals generated by the teacher.
An Axiomatic Characterization of Split Cycle
In this paper, we go further and show that Split Cycle is the only rule satisfying the axioms of Holliday and Pacuit together with two additional axioms, which characterize the class of rules that refine Split Cycle: Coherent Defeat and Positive Involvement in Defeat.
Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification
Classifying the sub-categories of an object from the same super-category (e. g., bird) in a fine-grained visual classification (FGVC) task highly relies on mining multiple discriminative features.
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
Ranked #31 on
Fine-Grained Image Classification
on FGVC Aircraft
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification
Classifying the sub-categories of an object from the same super-category (e. g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization.
Weakly Aggregative Modal Logic: Characterization and Interpolation (new version)
Weakly Aggregative Modal Logic (WAML) is a collection of disguised polyadic modal logics with n-ary modalities whose arguments are all the same.
Epistemic Logic with Functional Dependency Operator
In this paper, we propose a new operator Kf to express knowledge of the functional dependencies between variables.
