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07/26/2023
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy
The growing demand for accurate control in varying and unknown environme...
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06/05/2023
Learning from Noisy Labels Generated by Extremely Point Annotations for OCT Fluid Segmentation
Automatic segmentation of fluid in OCT (Optical Coherence Tomography) im...
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03/21/2023
Equiangular Basis Vectors
We propose Equiangular Basis Vectors (EBVs) for classification tasks. In...
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03/18/2023
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on...
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02/07/2023
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to r...
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10/07/2022
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
This paper targets at improving the generalizability of hypergraph neura...
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09/28/2022
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
In this paper, we propose Suppression-Enhancing Mask based attention and...
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01/04/2022
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations
Self-supervision is recently surging at its new frontier of graph learni...
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07/15/2021
Algorithmic insights on continual learning from fruit flies
Continual learning in computational systems is challenging due to catast...
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06/24/2021
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Designing novel protein sequences for a desired 3D topological fold is a...
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10/22/2020
Graph Contrastive Learning with Augmentations
Generalizable, transferrable, and robust representation learning on grap...
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06/16/2020
When Does Self-Supervision Help Graph Convolutional Networks?
Self-supervision as an emerging technique has been employed to train con...
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04/16/2020
Network-principled deep generative models for designing drug combinations as graph sets
Combination therapy has shown to improve therapeutic efficacy while redu...
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03/30/2020
L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Graph convolution networks (GCN) are increasingly popular in many applic...
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12/29/2019
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
Predicting compound-protein affinity is critical for accelerating drug d...
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12/28/2019
Energy-based Graph Convolutional Networks for Scoring Protein Docking Models
Structural information about protein-protein interactions, often missing...
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11/09/2019
Learning to Optimize in Swarms
Learning to optimize has emerged as a powerful framework for various opt...
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01/31/2019
Bayesian active learning for optimization and uncertainty quantification in protein docking
Motivation: Ab initio protein docking represents a major challenge for o...
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06/20/2018
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
Motivation: Drug discovery demands rapid quantification of compound-prot...
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03/20/2017
Learning Correspondence Structures for Person Re-identification
This paper addresses the problem of handling spatial misalignments due t...
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04/23/2015