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Boyu Wang

Topic trends: 32,543 papers · similarity ≥ 0.4 · year ≥ 2024 · Data sourced from Semantic Scholar

34,598 papers | Abstracts: 31,650 (91.5%) | Citations: 34,598 (100.0%) | arXiv: 26,074 (75.4%)

Built: Feb 15, 2026, 4:44 AM AMS

23

papers

665

total citations

papers (23)

Distribution Matching for Crowd Counting

NEURIPS 2020arXiv

On Learning Contrastive Representations for Learning With Noisy Labels

On Learning Fairness and Accuracy on Multiple Subgroups

NEURIPS 2022arXiv

Geometric Visual Similarity Learning in 3D Medical Image Self-Supervised Pre-Training

Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting

NEURIPS 2020arXiv

Weakly Supervised Object Localization As Domain Adaption

Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing

Generalizing across Temporal Domains with Koopman Operators

Homophily Enhanced Graph Domain Adaptation

Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control

Intersectional Unfairness Discovery

ZETA: Leveraging $Z$-order Curves for Efficient Top-$k$ Attention

FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning

Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression

Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation

Textualize Visual Prompt for Image Editing via Diffusion Bridge

Event-Driven Online Vertical Federated Learning

MABR: Multilayer Adversarial Bias Removal Without Prior Bias Knowledge

Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model

Dynamically Instance-Guided Adaptation: A Backward-Free Approach for Test-Time Domain Adaptive Semantic Segmentation

A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning

Active Vision for Early Recognition of Human Actions

ConFREE: Conflict-free Client Update Aggregation for Personalized Federated Learning