Heng-Tze Cheng

Wide & Deep Learning for Recommender Systems

2016 · arXiv (Cornell University)

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, …

SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets

2019

Reinforcement learning methods for recommender systems optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are required to deal with …

LaMDA: Language Models for Dialog Applications

2022 · arXiv (Cornell University)

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 and are pre-trained on 1.56T words of public dialog …

Gemini: A Family of Highly Capable Multimodal Models

2023 · arXiv (Cornell University)

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging …