1 code implementation • 25 Sep 2023 • Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, Tatsunori Hashimoto
Alongside the emulator, we develop an LM-based automatic safety evaluator that examines agent failures and quantifies associated risks.
no code implementations • 18 Nov 2022 • Yangjun Ruan, Saurabh Singh, Warren Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning.
no code implementations • 17 Feb 2022 • Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison
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.
2 code implementations • ICLR 2022 • Yangjun Ruan, Yann Dubois, Chris J. Maddison
Machine learning systems often experience a distribution shift between training and testing.
Ranked #38 on Image Classification on ObjectNet (using extra training data)
1 code implementation • ICLR Workshop Neural_Compression 2021 • Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison
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.
1 code implementation • ICLR 2020 • Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh
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.
21 code implementations • NeurIPS 2019 • Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu
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)
11 code implementations • 22 May 2019 • Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu
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).