Author name cluster

Meng Wu

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective

Lianghe Shi
Meng Wu
Huijie Zhang
Zekai Zhang
Molei Tao
Qing Qu

The widespread use of diffusion models has led to an abundance of AI-generated data, raising concerns about model collapse---a phenomenon in which recursive iterations of training on synthetic data lead to performance degradation. Prior work primarily characterizes this collapse via variance shrinkage or distribution shift, but these perspectives miss practical manifestations of model collapse. This paper identifies a transition from generalization to memorization during model collapse in diffusion models, where models increasingly replicate training data instead of generating novel content during iterative training on synthetic samples. This transition is directly driven by the declining entropy of the synthetic training data produced in each training cycle, which serves as a clear indicator of model degradation. Motivated by this insight, we propose an entropy-based data selection strategy to mitigate the transition from generalization to memorization and alleviate model collapse. Empirical results show that our approach significantly enhances visual quality and diversity in recursive generation, effectively preventing collapse.

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ICLR Conference 2022 Conference Paper

Multi-Critic Actor Learning: Teaching RL Policies to Act with Style

Siddharth Mysore
George Cheng
Yunqi Zhao
Kate Saenko
Meng Wu

Using a single value function (critic) shared over multiple tasks in Actor-Critic multi-task reinforcement learning (MTRL) can result in negative interference between tasks, which can compromise learning performance. Multi-Critic Actor Learning (MultiCriticAL) proposes instead maintaining separate critics for each task being trained while training a single multi-task actor. Explicitly distinguishing between tasks also eliminates the need for critics to learn to do so and mitigates interference between task-value estimates. MultiCriticAL is tested in the context of multi-style learning, a special case of MTRL where agents are trained to behave with different distinct behavior styles, and yields up to 56% performance gains over the single-critic baselines and even successfully learns behavior styles in cases where single-critic approaches may simply fail to learn. In a simulated real-world use case, MultiCriticAL enables learning policies that smoothly transition between multiple fighting styles on an experimental build of EA’s UFC game.

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IJCAI Conference 2019 Conference Paper

K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection

Yuxi Zhou
Shenda Hong
Junyuan Shang
Meng Wu
Qingyun Wang
Hongyan Li
Junqing Xie

Atrial Fibrillation (AF) is an abnormal heart rhythm which can trigger cardiac arrest and sudden death. Nevertheless, its interpretation is mostly done by medical experts due to high error rates of computerized interpretation. One study found that only about 66% of AF were correctly recognized from noisy ECGs. This is in part due to insufficient training data, class skewness, as well as semantical ambiguities caused by noisy segments in an ECG record. In this paper, we propose a K-margin-based Residual-Convolution-Recurrent neural network (K-margin-based RCR-net) for AF detection from noisy ECGs. In detail, a skewness-driven dynamic augmentation method is employed to handle the problems of data inadequacy and class imbalance. A novel RCR-net is proposed to automatically extract both long-term rhythm-level and local heartbeat-level characters. Finally, we present a K-margin-based diagnosis model to automatically focus on the most important parts of an ECG record and handle noise by naturally exploiting expected consistency among the segments associated for each record. The experimental results demonstrate that the proposed method with 0. 8125 F1NAOP score outperforms all state-of-the-art deep learning methods for AF detection task by 6. 8%.

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AAAI Conference 2018 Conference Paper

Horizontal Scaling With a Framework for Providing AI Solutions Within a Game Company

John Kolen
Mohsen Sardari
Marwan Mattar
Nick Peterson
Meng Wu

Games have been a major focus of AI since the ﬁeld formed seventy years ago. Recently, video games have replaced chess and go as the current “Mt. Everest Problem”. This paper looks beyond the video games themselves to the application of AI techniques within the ecosystems that produce them. Electronic Arts (EA) must deal with AI at scale across many game studios as it develops many AAA games each year, and not a single, AI-based, ﬂagship application. EA has adopted a horizontal scaling strategy in response to this challenge and built a platform for delivering AI artifacts anywhere within EA’s software universe. By combining a data warehouse for player history, an Agent Store for capturing processes acquired through machine learning, and a recommendation engine as an action layer, EA has been delivering a wide range of AI solutions throughout the company during the last two years. These solutions, such as dynamic difﬁculty adjustment, in-game content and activity recommendations, matchmaking, and game balancing, have had major impact on engagement, revenue, and development resources within EA.

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