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09/08/2023
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning
Real-world graphs naturally exhibit hierarchical or cyclical structures ...
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03/24/2023
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck
In practical scenarios where training data is limited, many predictive s...
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03/06/2023
Guiding Energy-based Models via Contrastive Latent Variables
An energy-based model (EBM) is a popular generative framework that offer...
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03/02/2023
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
Unsupervised meta-learning aims to learn generalizable knowledge across ...
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10/11/2022
Meta-Learning with Self-Improving Momentum Target
The idea of using a separately trained target model (or teacher) to impr...
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06/16/2022
Patch-level Representation Learning for Self-supervised Vision Transformers
Recent self-supervised learning (SSL) methods have shown impressive resu...
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11/18/2021
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Recent unsupervised representation learning methods have shown to be eff...
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06/09/2021
Self-Improved Retrosynthetic Planning
Retrosynthetic planning is a fundamental problem in chemistry for findin...
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05/03/2021
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Retrosynthesis, of which the goal is to find a set of reactants for synt...
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07/04/2020
Guiding Deep Molecular Optimization with Genetic Exploration
De novo molecular design attempts to search over the chemical space for ...
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10/14/2019
Rethinking Data Augmentation: Self-Supervision and Self-Distillation
Data augmentation techniques, e.g., flipping or cropping, which systemat...
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05/15/2019
Learning What and Where to Transfer
As the application of deep learning has expanded to real-world problems ...
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07/07/2018