Learning Genetic Perturbation Effects with Variational Causal Inference
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Papers and Publications
Leveraging Intermediate Neural Collapse with Simplex ETFs for Efficient Deep Neural Networks
Published:
This paper examines the phenomenon of neural collapse, where network activations, class means, and linear classifier weights converge to a simplex equiangular tight frame (ETF) during training, enhancing interpretability, robustness, and generalization. Building on findings that neural collapse extends beyond the final layer in fully connected networks, the authors propose two novel methods: Adaptive-ETF, which enforces simplex ETF constraints across all layers beyond a certain depth, and ETF-Transformer, which applies these constraints to feedforward layers in transformer blocks. Both methods maintain performance while significantly reducing trainable parameters, offering efficient alternatives for network training and regularization.
Recommended citation: Leveraging Intermediate Neural Collapse with Simplex ETFs for Efficient Deep Neural Networks (2024). Emily Liu. NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning.
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Semi-Supervised Neural Processes for Articulated Object Interaction
Published:
This paper addresses the challenge of limited labeled action data for robotic object manipulation by introducing the Semi-Supervised Neural Process (SSNP). SSNP combines small amounts of labeled interaction data with abundant unlabeled visual data, using a jointly trained reward-prediction and autoencoding framework to extract task-relevant features. This approach reduces the need for extensive retraining and computational resources while improving generalization. The model outperforms other semi-supervised methods in a door-opening task, achieving superior performance with significantly less data.
Recommended citation: Semi-Supervised Neural Processes for Articulated Object Interaction. Emily Liu, Michael Noseworthy, Nicholas Roy. RSS 2024 Workshop on Structural Priors as Inductive Biases for Learning Robot Dynamics.
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Structured Latent Variable Models for Articulated Object Interaction
Published:
This paper explores how a robot can learn a low-dimensional representation of doors from videos of them opening or closing, enabling the inference of door-related parameters and interaction outcomes. Instead of relying solely on labeled datasets, the study employs a semi-supervised approach using the Neural Statistician, a structured latent variable model that separates shared context-level variables (common across all images of the same door) from instance-level variables (specific to each image). The model effectively generates realistic door image embeddings, which outperform context-free baselines in tasks such as predicting door parameters and optimizing actions in a visual bandit door-opening scenario, demonstrating its utility for more efficient and accurate robotic interaction.
Recommended citation: Structured Latent Variable Models for Articulated Object Interaction. Emily Liu, Michael Noseworthy, Nicholas Roy. 2023.
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