708 citations · 1.1k across the 9 of their papers we have counts for
9 papers
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Yun He, Huaixiu Steven Zheng, Yi Tay +9
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way. Here, we explore the use of HyperNetworks to generate hyper-prompts: we pro…
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall +57
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
Sukhdeep S. Sodhi, Ellie Ka-In Chio, Ambarish Jash +18
As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic spee…
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
Tao Wu, Ellie Ka-In Chio, Heng-Tze Cheng +15
Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they…
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Ge Liu, Rui Wu, Heng-Tze Cheng +7
Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments. However, training deep RL model is challenging in real world applications such as…
Modeling Information Need of Users in Search Sessions
Kishaloy Halder, Heng-Tze Cheng, Ellie Ka In Chio +3
Users issue queries to Search Engines, and try to find the desired information in the results produced. They repeat this process if their information need is not met at the first p…
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
Eugene Ie, Vihan Jain, Jing Wang +10
Most practical recommender systems focus on estimating immediate user engagement without considering the long-term effects of recommendations on user behavior. Reinforcement learni…
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Heng-Tze Cheng, Zakaria Haque, Lichan Hong +12
We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in o…
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen +13
Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature…