Search Results for author: Pu Yang

Found 8 papers, 3 papers with code

Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Boostrapping

1 code implementation31 Jan 2025 Pu Yang, Yunzhen Feng, ZiYuan Chen, Yuhang Wu, Zhuoyuan Li

Modern foundation models often undergo iterative ``bootstrapping'' in their post-training phase: a model generates synthetic data, an external verifier filters out low-quality samples, and the high-quality subset is used for further fine-tuning.

Image Denoising Math

MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning

no code implementations3 Jan 2025 Pu Yang, Bin Dong

Image captioning is a critical task at the intersection of computer vision and natural language processing, with wide-ranging applications across various domains.

General Knowledge Image Captioning +2

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification

no code implementations11 Jun 2024 Yunzhen Feng, Elvis Dohmatob, Pu Yang, Francois Charton, Julia Kempe

Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of the pre-training corpus, or because synthetized data is used as a replacement for expensive human-annotation.

News Summarization

A Tale of Tails: Model Collapse as a Change of Scaling Laws

no code implementations10 Feb 2024 Elvis Dohmatob, Yunzhen Feng, Pu Yang, Francois Charton, Julia Kempe

We discover a wide range of decay phenomena, analyzing loss of scaling, shifted scaling with number of generations, the ''un-learning" of skills, and grokking when mixing human and synthesized data.

Language Modeling Language Modelling +2

Climate uncertainty impacts on optimal mitigation pathways and social cost of carbon

1 code implementation18 Apr 2023 Christopher J. Smith, Alaa Al Khourdajie, Pu Yang, Doris Folini

CO2 emissions trajectories resulting from the optimal level of emissions abatement in all pathways are also sensitive to climate uncertainty, with 2050 emissions ranging from -12 to +14 GtCO2/yr in the 1. 5C scenario.

L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement Learning

1 code implementation5 Dec 2022 Pu Yang, Bin Dong

In this paper, we propose an alternating training framework for jointly learning a good pair of samplers and reconstructors via deep reinforcement learning (RL).

Deep Reinforcement Learning Reinforcement Learning (RL)

A frequency domain analysis of gradient-based adversarial examples

no code implementations1 Jan 2021 Bochen Lv, Pu Yang, Zehao Wang, Zhanxing Zhu

And the log-spectrum difference of the adversarial examples and clean image is more concentrated in the high-frequency part than the low-frequency part.

Deep Multi-Modal Sets

no code implementations3 Mar 2020 Austin Reiter, Menglin Jia, Pu Yang, Ser-Nam Lim

Most deep learning-based methods rely on a late fusion technique whereby multiple feature types are encoded and concatenated and then a multi layer perceptron (MLP) combines the fused embedding to make predictions.

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