Search Results for author:
Found 7 papers, 2 papers with code
Ad Recommendation in a Collapsed and Entangled World
In this paper, we present an industry ad recommendation system, paying attention to the challenges and practices of learning appropriate representations.
Code-Switching Text Generation and Injection in Mandarin-English ASR
Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter.
On Provably Robust Meta-Bayesian Optimization
We prove that both algorithms are asymptotically no-regret even when some or all previous tasks are dissimilar to the current task, and show that RM-GP-UCB enjoys a better theoretical robustness than RM-GP-TS.
Convolutional Normalizing Flows for Deep Gaussian Processes
Deep Gaussian processes (DGPs), a hierarchical composition of GP models, have successfully boosted the expressive power of their single-layer counterpart.
Implicit Posterior Variational Inference for Deep Gaussian Processes
This paper presents an implicit posterior variational inference (IPVI) framework for DGPs that can ideally recover an unbiased posterior belief and still preserve time efficiency.
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises.
