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pro vyhledávání: '"Gu, Bin"'
Bilevel optimization (BO) has recently gained prominence in many machine learning applications due to its ability to capture the nested structure inherent in these problems. Recently, many hypergradient methods have been proposed as effective solutio
Externí odkaz:
http://arxiv.org/abs/2406.17386
Evolution Strategies (ES) have emerged as a competitive alternative for model-free reinforcement learning, showcasing exemplary performance in tasks like Mujoco and Atari. Notably, they shine in scenarios with imperfect reward functions, making them
Externí odkaz:
http://arxiv.org/abs/2405.01615
Large language models (LLMs) has experienced exponential growth, they demonstrate remarkable performance across various tasks. Notwithstanding, contemporary research primarily centers on enhancing the size and quality of pretraining data, still utili
Externí odkaz:
http://arxiv.org/abs/2404.08885
Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based me
Externí odkaz:
http://arxiv.org/abs/2404.01897
Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes. However, this potential comes with inherent challenges in directly
Externí odkaz:
http://arxiv.org/abs/2403.18388
Recent statements about the impressive capabilities of large language models (LLMs) are usually supported by evaluating on open-access benchmarks. Considering the vast size and wide-ranging sources of LLMs' training data, it could explicitly or impli
Externí odkaz:
http://arxiv.org/abs/2402.15938
Autor:
Li, Loka, Ng, Ignavier, Luo, Gongxu, Huang, Biwei, Chen, Guangyi, Liu, Tongliang, Gu, Bin, Zhang, Kun
Conventional causal discovery methods rely on centralized data, which is inconsistent with the decentralized nature of data in many real-world situations. This discrepancy has motivated the development of federated causal discovery (FCD) approaches.
Externí odkaz:
http://arxiv.org/abs/2402.13241
Pairwise learning, an important domain within machine learning, addresses loss functions defined on pairs of training examples, including those in metric learning and AUC maximization. Acknowledging the quadratic growth in computation complexity acco
Externí odkaz:
http://arxiv.org/abs/2402.01146
Autor:
Li, Jia, Li, Ge, Zhao, Yunfei, Li, Yongmin, Jin, Zhi, Zhu, Hao, Liu, Huanyu, Liu, Kaibo, Wang, Lecheng, Fang, Zheng, Wang, Lanshen, Ding, Jiazheng, Zhang, Xuanming, Dong, Yihong, Zhu, Yuqi, Gu, Bin, Yang, Mengfei
How to evaluate Large Language Models (LLMs) in code generation is an open question. Many benchmarks have been proposed but are inconsistent with practical software projects, e.g., unreal program distributions, insufficient dependencies, and small-sc
Externí odkaz:
http://arxiv.org/abs/2401.06401
Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive (or even unknown) applicability conditions, or high computational cost
Externí odkaz:
http://arxiv.org/abs/2401.05394