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Found 9 papers, 4 papers with code
The Implicit Bias of Heterogeneity towards Invariance and Causality
It is observed empirically that the large language models (LLM), trained with a variant of regression loss using numerous corpus from the Internet, can unveil causal associations to some extent.
Environment Invariant Linear Least Squares
To the best of our knowledge, this paper is the first to realize statistically efficient invariance learning in the general linear model.
How do noise tails impact on deep ReLU networks?
This paper investigates the stability of deep ReLU neural networks for nonparametric regression under the assumption that the noise has only a finite p-th moment.
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks
With the help of a new technique called {\it neural network grafting}, we demonstrate that even during the entire training process, feature distributions of differently initialized networks remain similar at each layer.
Convex Formulation of Overparameterized Deep Neural Networks
This new analysis is consistent with empirical observations that deep neural networks are capable of learning efficient feature representations.
Domain Adaptive Imitation Learning
We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch.
Cross Domain Imitation Learning
Informally, CDIL is the process of learning how to perform a task optimally, given demonstrations of the task in a distinct domain.
Language Modeling with Sparse Product of Sememe Experts
Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words.
ZhuSuan: A Library for Bayesian Deep Learning
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.
