5 papers · 1 filter
Ensuring Semantics in Weights of Implicit Neural Representations through the Implicit Function Theorem
Tianming Qiu, Christos Sonis, Hao Shen
Weight Space Learning (WSL), which frames neural network weights as a data modality, is an emerging field with potential for tasks like meta-learning or transfer learning. Particul…
An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
Uzair Akbar, Niki Kilbertus, Hao Shen +2
The technique of data augmentation (DA) is often used in machine learning for regularization purposes to better generalize under i.i.d. settings. In this work, we present a unifyin…
Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors
Zhiwei Han, Stefan Matthes, Hao Shen
In this work, we establish the sufficient conditions under which nonlinear Canonical Correlation Analysis (CCA) recovers ground-truth latent factors up to an affine transformation.…
Mechanistic Independence: A Principle for Identifiable Disentangled Representations
Stefan Matthes, Zhiwei Han, Hao Shen
Disentangled representations seek to recover latent factors of variation underlying observed data, yet their identifiability is still not fully understood. We introduce a unified f…
Towards a Unified Framework of Contrastive Learning for Disentangled Representations
Stefan Matthes, Zhiwei Han, Hao Shen
Contrastive learning has recently emerged as a promising approach for learning data representations that discover and disentangle the explanatory factors of the data. Previous anal…