Search Results for author:
Found 9 papers, 7 papers with code
ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback
The work presents our practices of aligning LLMs with human preferences, offering insights into the challenges and solutions in RLHF implementations.
OctoPack: Instruction Tuning Code Large Language Models
We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46. 2% pass@1).
Ranked #4 on
Code Generation
on HumanEval
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X
Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.
Ranked #81 on
Code Generation
on MBPP
GIPA: A General Information Propagation Algorithm for Graph Learning
Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.
TDGIA:Effective Injection Attacks on Graph Neural Networks
In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.
GIPA: General Information Propagation Algorithm for Graph Learning
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques
Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques
This report is about applications based on spatial-frequency transform and deep learning techniques.
