144 citations · 243 across the 64 of their papers we have counts for
9 papers · 1 filter
Agentic Reverse Engineering: From I/O to Code with Discovery Agent
Yihong Dong, Jiaru Qian, Haoran Zhang +7
The automatic synthesis of a program from any form of specification is regarded as a holy grail of computer science. Fueled by LLMs, NL2Code has achieved tremendous success, yet th…
Do Transformers Have the Ability for Periodicity Generalization?
Huanyu Liu, Ge Li, Yihong Dong +7
Large language models (LLMs) based on the Transformer have demonstrated strong performance across diverse tasks. However, current models still exhibit substantial limitations in ou…
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning
Huanyu Liu, Jia Li, Yihong Dong +6
Reinforcement learning with verifiable reward (RLVR) has become a promising paradigm for post-training large language models (LLMs) to improve their reasoning capability. However,…
SATURN: SAT-based Reinforcement Learning to Unleash LLMs Reasoning
Huanyu Liu, Ge Li, Jia Li +3
How to design reinforcement learning (RL) tasks that effectively unleash the reasoning capability of large language models (LLMs) remains an open question. Existing RL tasks (e.g.,…
FAN: Fourier Analysis Networks
Yihong Dong, Ge Li, Yongding Tao +7
Despite the remarkable successes of general-purpose neural networks, such as MLPs and Transformers, we find that they exhibit notable shortcomings in modeling and reasoning about p…
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
Khondoker Murad Hossain, Tim Oates
As deep neural networks and the datasets used to train them get larger, the default approach to integrating them into research and commercial projects is to download a pre-trained…
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
Xiaolong Xu, Lingjuan Lyu, Yihong Dong +3
With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels w…
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition
Yihong Dong, Ying Peng, Muqiao Yang +2
Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for identifying the nonlinear feature structure…
SR2CNN: Zero-Shot Learning for Signal Recognition
Yihong Dong, Xiaohan Jiang, Huaji Zhou +2
Signal recognition is one of significant and challenging tasks in the signal processing and communications field. It is often a common situation that there's no training data acces…