Dongxiao Zhu
14 papers · 2020–2026 · 6 conferences · across top CS/AI conferences
Achievements
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π Cross-Pollinator (5) π Conference Polyglot (5) π Academic Marathon (5) π§ Keyword Pioneer π Renaissance Researcher (6)
πΊοΈ
Taxonomy Completionist
(31)
π
Conference Polyglot
(5)
π
Keyword Champion
(2)
π§¬
Topic Evolution
π
Century Club
(12)
ποΈ
Keyword Collector
(66)
π₯
Unstoppable
(6)
Conferences
AAAI (5)
IJCAI (4)
WACV (2)
ECCV (1)
MIDL (1)
NIPS (1)
Top co-authors
Keywords
deep neural network
(3)
neural network
(2)
gradient attribution
(2)
computed tomography
(2)
convolutional neural network
(2)
feature importance
(2)
medical image segmentation
(2)
collaborative filtering
(1)
adversarial robustness
(1)
transformer architecture
(1)
preference optimization
(1)
machine unlearning
(1)
neural network interpretability
(1)
feature attribution
(1)
data augmentation
(1)
stochastic gradient descent
(1)
model interpretability
(1)
class imbalance
(1)
multi-class classification
(1)
attention mechanism
(1)
Papers
Not All Tokens Are Meant to Be Forgotten
AAAI 2026
FluenceFormer: Transformer-Driven Multi-Beam Fluence Map Regression for Radiotherapy Planning
MIDL 2026
AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation
WACV 2025
MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training
WACV 2025
MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks
AAAI 2024
Fairness-aware Vision Transformer via Debiased Self-Attention
ECCV 2024
Negative Flux Aggregation to Estimate Feature Attributions
IJCAI 2023
Learning Compact Features via In-Training Representation Alignment
AAAI 2023
AttCAT: Explaining Transformers via Attentive Class Activation Tokens
NIPS 2022
Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN
IJCAI 2022
Explaining Deep Neural Network Models with Adversarial Gradient Integration
IJCAI 2021
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints
AAAI 2021
Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability
IJCAI 2020
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
AAAI 2020