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Found 41 papers, 21 papers with code
Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-Aware Learning
The training framework of the MPNN is constructed by a teacher-student architecture to learn segmentation from clean pixels and noisy pixels.
Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval
Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i. e., the same sub-category labels) highest based on the fine-grained details in the query.
MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries
Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images.
FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer
However, these methods only integrate long-range context information among keypoints with a fixed receptive field, which constrains the network from reconciling the importance of features with different receptive fields to realize complete image perception, hence limiting the matching accuracy.
Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field.
Current and future directions in network biology
As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs).
Geometric Pooling: maintaining more useful information
However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain useful information and can contribute considerably to the final decision.
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation
Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations.
Equiangular Basis Vectors
The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space.
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, have gained considerable attention due to their exceptional natural language processing capabilities.
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks.
Ranked #3 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Automatic Check-Out via Prototype-based Classifier Learning from Single-Product Exemplars
Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images).
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL).
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks.
A Channel Mix Method for Fine-Grained Cross-Modal Retrieval
In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities.
Cone-constrained Monotone Mean-Variance Portfolio Selection Under Diffusion Models
We consider monotone mean-variance (MMV) portfolio selection problems with a conic convex constraint under diffusion models, and their counterpart problems under mean-variance (MV) preferences.
Webly-Supervised Fine-Grained Recognition with Partial Label Learning
The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e. g., different bird species)by utilizing freely available but noisy web data. As the label noises signifcantly hurt the network training, it is desirable to distinguish and eliminate noisy images.
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations
Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation.
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval
Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations.
Mean-Variance Portfolio Selection in Contagious Markets
We consider a mean-variance portfolio selection problem in a financial market with contagion risk.
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.
Algorithmic insights on continual learning from fruit flies
We discovered a two layer neural circuit in the fruit fly olfactory system that addresses this challenge by uniquely combining sparse coding and associative learning.
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 fundamental yet non-trivial task in protein engineering.
Graph Contrastive Learning Automated
Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data.
Mean-Variance Investment and Risk Control Strategies -- A Time-Consistent Approach via A Forward Auxiliary Process
By introducing a deterministic auxiliary process defined forward in time, we formulate an alternative time-consistent problem related to the original MV problem, and obtain the optimal strategy and the value function to the new problem in closed-form.
Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty
Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.
Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction
Compound-protein pairs dominate FDA-approved drug-target pairs and the prediction of compound-protein affinity and contact (CPAC) could help accelerate drug discovery.
Graph Contrastive Learning with Augmentations
In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data.
When Does Self-Supervision Help Graph Convolutional Networks?
We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning.
Network-principled deep generative models for designing drug combinations as graph sets
We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer.
L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets.
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
DeepRelations shows superior interpretability to the state-of-the-art: without compromising affinity prediction, it boosts the AUPRC of contact prediction 9. 5, 16. 9, 19. 3 and 5. 7-fold for the test, compound-unique, protein-unique, and both-unique sets, respectively.
Energy-based Graph Convolutional Networks for Scoring Protein Docking Models
Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking.
Learning to Optimize in Swarms
Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks.
Bayesian active learning for optimization and uncertainty quantification in protein docking
To the best of our knowledge, this study represents the first uncertainty quantification solution for protein docking, with theoretical rigor and comprehensive assessment.
Egocentric Activity Prediction via Event Modulated Attention
Predicting future activities from an egocentric viewpoint is of particular interest in assisted living.
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI).
Ranked #2 on
Drug Discovery
on BindingDB IC50
Learning Correspondence Structures for Person Re-identification
We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.
Person Re-identification with Correspondence Structure Learning
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.
