Search Results for author: Benben Liao

Found 11 papers, 5 papers with code

ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution

1 code implementation19 May 2022 Lixue Cheng, ZiYi Yang, Benben Liao, ChangYu Hsieh, Shengyu Zhang

Directed evolution is a versatile technique in protein engineering that mimics the process of natural selection by iteratively alternating between mutagenesis and screening in order to search for sequences that optimize a given property of interest, such as catalytic activity and binding affinity to a specified target.

Experimental Design Outlier Detection

Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer

no code implementations29 Jan 2022 Yue Wan, Benben Liao, Chang-Yu Hsieh, Shengyu Zhang

In this paper, we propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction without relying on any cheminformatics tools for molecule editing.

Towards Disentangling Non-Robust and Robust Components in Performance Metric

no code implementations25 Sep 2019 Yujun Shi, Benben Liao, Guangyong Chen, Yun Liu, Ming-Ming Cheng, Jiashi Feng

Then, we show by experiments that DNNs under standard training rely heavily on optimizing the non-robust component in achieving decent performance.

Adversarial Robustness

Wasserstein Collaborative Filtering for Item Cold-start Recommendation

no code implementations10 Sep 2019 Yitong Meng, Guangyong Chen, Benben Liao, Jun Guo, Weiwen Liu

We further adopt the idea of CF and propose Wasserstein CF (WCF) to improve the recommendation performance on cold-start items.

Collaborative Filtering

PMD: An Optimal Transportation-based User Distance for Recommender Systems

no code implementations10 Sep 2019 Yitong Meng, Xinyan Dai, Xiao Yan, James Cheng, Weiwen Liu, Benben Liao, Jun Guo, Guangyong Chen

Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users.

Collaborative Filtering Recommendation Systems

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

1 code implementation22 Jun 2019 Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard Zemel, Shengyu Zhang

We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science.

14

A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR

no code implementations13 Jun 2019 Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang

Most recent efforts have been devoted to defending noisy labels by discarding noisy samples from the training set or assigning weights to training samples, where the weight associated with a noisy sample is expected to be small.

Data Augmentation

Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric

no code implementations6 Jun 2019 Yujun Shi, Benben Liao, Guangyong Chen, Yun Liu, Ming-Ming Cheng, Jiashi Feng

Despite many previous works studying the reason behind such adversarial behavior, the relationship between the generalization performance and adversarial behavior of DNNs is still little understood.

Adversarial Robustness

Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks

1 code implementation15 May 2019 Guangyong Chen, Pengfei Chen, Yujun Shi, Chang-Yu Hsieh, Benben Liao, Shengyu Zhang

Our work is based on an excellent idea that whitening the inputs of neural networks can achieve a fast convergence speed.

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

1 code implementation13 May 2019 Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang

Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the noisy labels.

Image Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.