Search Results for author:
Found 13 papers, 2 papers with code
AudioPaLM: A Large Language Model That Can Speak and Listen
AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2.
Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models
Learned metrics such as BLEURT have in recent years become widely employed to evaluate the quality of machine translation systems.
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations
We show that adding a consistency loss, ensuring that the predictions of the network are consistent over consecutive training epochs, is a simple yet effective method to train multi-label classifiers in a weakly supervised setting.
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
We study the problem of causal discovery through targeted interventions.
On Mutual Information Maximization for Representation Learning
Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data.
Practical and Consistent Estimation of f-Divergences
The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning.
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
In contrast to known identifiability results for nonlinear ICA, we prove that independent latent sources with arbitrary mixing can be recovered as long as multiple, sufficiently different noisy views are available.
An Empirical Study of Generative Models with Encoders
Generative adversarial networks (GANs) are capable of producing high quality image samples.
On the Latent Space of Wasserstein Auto-Encoders
We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs).
Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail.
Probabilistic Active Learning of Functions in Structural Causal Models
We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified.
From Deterministic ODEs to Dynamic Structural Causal Models
Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood.
A Kernel Test for Three-Variable Interactions with Random Processes
The main contributions of this paper are twofold: first, we prove that the Lancaster statistic satisfies the conditions required to estimate the quantiles of the null distribution using the wild bootstrap; second, the manner in which this is proved is novel, simpler than existing methods, and can further be applied to other statistics.
