Search Results for author:
Found 8 papers, 1 papers with code
Reinforced Self-Training (ReST) for Language Modeling
Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences.
Optimizing Memory Mapping Using Deep Reinforcement Learning
We also introduce a Reinforcement Learning agent, mallocMuZero, and show that it is capable of playing this game to discover new and improved memory mapping solutions that lead to faster execution times on real ML workloads on ML accelerators.
MuZero with Self-competition for Rate Control in VP9 Video Compression
Specifically, we target the problem of learning a rate control policy to select the quantization parameters (QP) in the encoding process of libvpx, an open source VP9 video compression library widely used by popular video-on-demand (VOD) services.
More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech
In this paper we present VDTTS, a Visually-Driven Text-to-Speech model.
Neural Rate Control for Video Encoding using Imitation Learning
In modern video encoders, rate control is a critical component and has been heavily engineered.
Large-scale multilingual audio visual dubbing
The visual content is translated by synthesizing lip movements for the speaker to match the translated audio, creating a seamless audiovisual experience in the target language.
How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one.
Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching
We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue.
