Search Results for author: Yuxing Peng

Found 20 papers, 13 papers with code

GenMol: A Drug Discovery Generalist with Discrete Diffusion

no code implementations10 Jan 2025 Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Yuxing Peng, Saee Paliwal, Weili Nie, Arash Vahdat

Drug discovery is a complex process that involves multiple scenarios and stages, such as fragment-constrained molecule generation, hit generation and lead optimization.

Computational Efficiency Drug Discovery +1

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery

no code implementations15 Nov 2024 Peter St. John, Dejun Lin, Polina Binder, Malcolm Greaves, Vega Shah, John St. John, Adrian Lange, Patrick Hsu, Rajesh Illango, Arvind Ramanathan, Anima Anandkumar, David H Brookes, Akosua Busia, Abhishaike Mahajan, Stephen Malina, Neha Prasad, Sam Sinai, Lindsay Edwards, Thomas Gaudelet, Cristian Regep, Martin Steinegger, Burkhard Rost, Alexander Brace, Kyle Hippe, Luca Naef, Keisuke Kamata, George Armstrong, Kevin Boyd, Zhonglin Cao, Han-Yi Chou, Simon Chu, Allan dos Santos Costa, Sajad Darabi, Eric Dawson, Kieran Didi, Cong Fu, Mario Geiger, Michelle Gill, Darren Hsu, Gagan Kaushik, Maria Korshunova, Steven Kothen-Hill, Youhan Lee, Meng Liu, Micha Livne, Zachary McClure, Jonathan Mitchell, Alireza Moradzadeh, Ohad Mosafi, Youssef Nashed, Yuxing Peng, Sara Rabhi, Farhad Ramezanghorbani, Danny Reidenbach, Camir Ricketts, Brian Roland, Kushal Shah, Tyler Shimko, Hassan Sirelkhatim, Savitha Srinivasan, Abraham C Stern, Dorota Toczydlowska, Srimukh Prasad Veccham, Niccolò Alberto Elia Venanzi, Anton Vorontsov, Jared Wilber, Isabel Wilkinson, Wei Jing Wong, Eva Xue, Cory Ye, Xin Yu, Yang Zhang, Guoqing Zhou, Becca Zandstein, Christian Dallago, Bruno Trentini, Emine Kucukbenli, Saee Paliwal, Timur Rvachov, Eddie Calleja, Johnny Israeli, Harry Clifford, Risto Haukioja, Nicholas Haemel, Kyle Tretina, Neha Tadimeti, Anthony B Costa

We introduce the BioNeMo Framework to facilitate the training of computational biology and chemistry AI models across hundreds of GPUs.

Drug Discovery

Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration

1 code implementation CVPR 2023 Zheng Qin, Hao Yu, Changjian Wang, Yuxing Peng, Kai Xu

We first design a local spatial consistency measure over the deformation graph of the point cloud, which evaluates the spatial compatibility only between the correspondences in the vicinity of a graph node.

Point Cloud Registration

Audio Tagging by Cross Filtering Noisy Labels

no code implementations16 Jul 2020 Boqing Zhu, Kele Xu, Qiuqiang Kong, Huaimin Wang, Yuxing Peng

Yet, it is labor-intensive to accurately annotate large amount of audio data, and the dataset may contain noisy labels in the practical settings.

Audio Tagging Memorization +1

A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning

1 code implementation IJCNLP 2019 Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li

Rapid progress has been made in the field of reading comprehension and question answering, where several systems have achieved human parity in some simplified settings.

Negation Question Answering +1

ThunderNet: Towards Real-time Generic Object Detection

3 code implementations28 Mar 2019 Zheng Qin, Zeming Li, Zhaoning Zhang, Yiping Bao, Gang Yu, Yuxing Peng, Jian Sun

In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight two-stage detector named ThunderNet.

Object object-detection +1

Attention-Guided Answer Distillation for Machine Reading Comprehension

no code implementations EMNLP 2018 Minghao Hu, Yuxing Peng, Furu Wei, Zhen Huang, Dongsheng Li, Nan Yang, Ming Zhou

Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models.

Knowledge Distillation Machine Reading Comprehension

Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions

3 code implementations27 Mar 2018 Zheng Qin, Zhaoning Zhang, Dongsheng Li, Yiming Zhang, Yuxing Peng

Depthwise convolutions provide significant performance benefits owing to the reduction in both parameters and mult-adds.

Learning Environmental Sounds with Multi-scale Convolutional Neural Network

1 code implementation25 Mar 2018 Boqing Zhu, Changjian Wang, Feng Liu, Jin Lei, Zengquan Lu, Yuxing Peng

For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce two-phase method to fuse the different features.

Sound Audio and Speech Processing

Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications

2 code implementations24 Mar 2018 Zheng Qin, Zhaoning Zhang, Shiqing Zhang, Hao Yu, Yuxing Peng

Compact neural networks are inclined to exploit "sparsely-connected" convolutions such as depthwise convolution and group convolution for employment in mobile applications.

FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy

3 code implementations11 Feb 2018 Zheng Qin, Zhaoning Zhang, Xiaotao Chen, Yuxing Peng

Experiments on ILSVRC 2012 and PASCAL VOC 2007 datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing MobileNet by 5. 5% on the ILSVRC 2012 top-1 accuracy and 3. 6% on the VOC 2007 mAP under a complexity of 12 MFLOPs.

Reinforced Mnemonic Reader for Machine Reading Comprehension

3 code implementations8 May 2017 Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, Ming Zhou

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.

Machine Reading Comprehension Question Answering +3

hi-RF: Incremental Learning Random Forest for large-scale multi-class Data Classification

no code implementations31 Aug 2016 Ting-Ting Xie, Yuxing Peng, Changjian Wang

Most traditional methods struggle to balance the precision and computational burden when data and its number of classes increased.

Computational Efficiency General Classification +1

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