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Found 27 papers, 14 papers with code
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models.
GraphCodeBERT: Pre-training Code Representations with Data Flow
Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.
Ranked #3 on
Type prediction
on ManyTypes4TypeScript
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task.
Automating Code Review Activities by Large-Scale Pre-training
In this research, we focus on utilizing pre-training techniques for the tasks in the code review scenario.
Code Execution with Pre-trained Language Models
Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks.
Ranked #1 on
Cloze Test
on CodeXGLUE - CT-maxmin
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92. 5% accuracy on the English test of the Chinese national college entrance exam.
ReACC: A Retrieval-Augmented Code Completion Framework
Code completion, which aims to predict the following code token(s) according to the code context, can improve the productivity of software development.
Im2win: Memory Efficient Convolution On SIMD Architectures
However, the im2col data transformation can lead to at least 2$\times$ memory footprint compared to not using data transformation at all, thus limiting the size of neural network models running on memory-limited systems.
Im2win: An Efficient Convolution Paradigm on GPU
Convolution is the most time-consuming operation in deep neural network operations, so its performance is critical to the overall performance of the neural network.
Constructing Multilingual Code Search Dataset Using Neural Machine Translation
Code search is a task to find programming codes that semantically match the given natural language queries.
Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach
Second, we propose a physics-informed estimator for the AGM that is robust to low-quality measurements, in which the physical constraints associated with the parameter normalization and sparsity are embedded to improve the accuracy and robustness.
On the Solution Uniqueness of Data-Driven Modeling of Flexible Loads
To address this, we analyze the structural and practical identifiability of the PFL model, deriving the condition for the solution uniqueness.
Analysis of regularized Nyström subsampling for regression functions of low smoothness
This paper studies a Nystr\"om type subsampling approach to large kernel learning methods in the misspecified case, where the target function is not assumed to belong to the reproducing kernel Hilbert space generated by the underlying kernel.
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation
This paper addresses the question: Why do neural dialog systems generate short and meaningless replies?
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction.
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well.
Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency
We propose the Multi-Perspective Self-Consistency (MPSC) framework incorporating both inter- and intra-consistency across outputs from multiple perspectives.
Privacy-Preserved Aggregate Thermal Dynamic Model of Buildings
The thermal inertia of buildings brings considerable flexibility to the heating and cooling load, which is known to be a promising demand response resource.
Competition-Level Problems are Effective LLM Evaluators
Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about these abilities and the potential data contamination problem recently.
Semi-implicit Continuous Newton Method for Power Flow Analysis
This paper proposes a semi-implicit version of continuous Newton method (CNM) for power flow analysis.
GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments
This paper introduces GAgent: an Gripping Agent designed for open-world environments that provides advanced cognitive abilities via VLM agents and flexible grasping abilities with variable stiffness soft grippers.
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
We propose Class-Agnostic Distribution Alignment (CADA) to align the mismatched score distribution of each implicit class without knowing class information, which enables unified anomaly detection for all classes and samples.
