2 citations · 4 across the 13 of their papers we have counts for
13 papers
A Critical Review of Predominant Bias in Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Bias issues of neural networks garner significant attention along with its promising advancement. Among various bias issues, mitigating two predominant biases is crucial in advanci…
Look, Learn and Leverage (L): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment
Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei +2
Modern deep learning models have demonstrated outstanding performance on discovering the underlying mechanisms when both visual appearance and intrinsic relations (e.g., causal str…
An Investigation on The Position Encoding in Vision-Based Dynamics Prediction
Jiageng Zhu, Hanchen Xie, Jiazhi Li +2
Despite the success of vision-based dynamics prediction models, which predict object states by utilizing RGB images and simple object descriptions, they were challenged by environm…
DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
Jiageng Zhu, Hanchen Xie, Jiazhi Li +1
Accurate estimation of counterfactual outcomes in high-dimensional data is crucial for decision-making and understanding causal relationships and intervention outcomes in various d…
SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for prediction is crucial in advancing fair and trustworthy AI. While several promising meth…
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI. While several promising met…
Shadow Datasets, New challenging datasets for Causal Representation Learning
Jiageng Zhu, Hanchen Xie, Jianhua Wu +4
Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which i…
A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei +3
Dynamics prediction, which is the problem of predicting future states of scene objects based on current and prior states, is drawing increasing attention as an instance of learning…
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping
Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed
Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representati…
Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factors
Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed
Disentangled and invariant representations are two critical goals of representation learning and many approaches have been proposed to achieve either one of them. However, those tw…
Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength
Jiageng Zhu, Hanchen Xie, Wael AbdAlmageed
Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data. However, existing methods suffer from merely using…
Partner-Assisted Learning for Few-Shot Image Classification
Jiawei Ma, Hanchen Xie, Guangxing Han +3
Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learn…
MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization
Hanchen Xie, Mohamed E. Hussein, Aram Galstyan +1
Deep neural networks are powerful, massively parameterized machine learning models that have been shown to perform well in supervised learning tasks. However, very large amounts of…