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Found 8 papers, 6 papers with code
FastSpeech: Fast,Robustand Controllable Text-to-Speech
Compared with traditional concatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i. e., some words are skipped or repeated) and lack of controllability (voice speed or prosody control).
FastSpeech: Fast, Robust and Controllable Text to Speech
In this work, we propose a novel feed-forward network based on Transformer to generate mel-spectrogram in parallel for TTS.
Ranked #10 on
Text-To-Speech Synthesis
on LJSpeech
(using extra training data)
Learning to Learn by Zeroth-Order Oracle
In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules.
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Naively applied, our schemes would require more initial bits than the standard bits-back coder, but we show how to drastically reduce this additional cost with couplings in the latent space.
Optimal Representations for Covariate Shift
Machine learning systems often experience a distribution shift between training and testing.
Ranked #38 on
Image Classification
on ObjectNet
(using extra training data)
Augment with Care: Contrastive Learning for Combinatorial Problems
While typical graph contrastive pre-training uses label-agnostic augmentations, our key insight is that many combinatorial problems have well-studied invariances, which allow for the design of label-preserving augmentations.
Weighted Ensemble Self-Supervised Learning
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning.
Identifying the Risks of LM Agents with an LM-Emulated Sandbox
Alongside the emulator, we develop an LM-based automatic safety evaluator that examines agent failures and quantifies associated risks.
