46 citations · 68 across the 8 of their papers we have counts for
8 papers
A Latent Diffusion Model for Protein Structure Generation
Cong Fu, Keqiang Yan, Limei Wang +7
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms. Designing and generating novel proteins can pave the way for many future synt…
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
Shuyu Dong, Kento Uemura, Akito Fujii +4
Learning causal structures from observational data is a fundamental problem facing important computational challenges when the number of variables is large. In the context of linea…
Generating 3D Molecules for Target Protein Binding
Meng Liu, Youzhi Luo, Kanji Uchino +2
A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and ef…
Automated Data Augmentations for Graph Classification
Youzhi Luo, Michael McThrow, Wing Yee Au +4
Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations tha…
Crowdsourcing Evaluation of Saliency-based XAI Methods
Xiaotian Lu, Arseny Tolmachev, Tatsuya Yamamoto +5
Understanding the reasons behind the predictions made by deep neural networks is critical for gaining human trust in many important applications, which is reflected in the increasi…
Inter-domain Multi-relational Link Prediction
Luu Huu Phuc, Koh Takeuchi, Seiji Okajima +4
Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities. Similar to o…
Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures
Arseny Tolmachev, Akira Sakai, Masaru Todoriki +1
Most graph neural network architectures work by message-passing node vector embeddings over the adjacency matrix, and it is assumed that they capture graph topology by doing that.…
Linear Tensor Projection Revealing Nonlinearity
Koji Maruhashi, Heewon Park, Rui Yamaguchi +1
Dimensionality reduction is an effective method for learning high-dimensional data, which can provide better understanding of decision boundaries in human-readable low-dimensional…