5.7k citations · 8.2k across the 48 of their papers we have counts for
10 papers · 1 filter
Understanding the performance gap between online and offline alignment algorithms
Yunhao Tang, Daniel Zhaohan Guo, Zeyu Zheng +8
Reinforcement learning from human feedback (RLHF) is the canonical framework for large language model alignment. However, rising popularity in offline alignment algorithms challeng…
Can Public Large Language Models Help Private Cross-device Federated Learning?
Boxin Wang, Yibo Jacky Zhang, Yuan Cao +5
We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful form…
MUX-PLMs: Data Multiplexing for High-throughput Language Models
Vishvak Murahari, Ameet Deshpande, Carlos E. Jimenez +4
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasi…
Efficient and Private Federated Learning with Partially Trainable Networks
Hakim Sidahmed, Zheng Xu, Ankush Garg +2
Federated learning is used for decentralized training of machine learning models on a large number (millions) of edge mobile devices. It is challenging because mobile devices often…
Understanding How Encoder-Decoder Architectures Attend
Kyle Aitken, Vinay V Ramasesh, Yuan Cao +1
Encoder-decoder networks with attention have proven to be a powerful way to solve many sequence-to-sequence tasks. In these networks, attention aligns encoder and decoder states an…
Towards NNGP-guided Neural Architecture Search
Daniel S. Park, Jaehoon Lee, Daiyi Peng +2
The predictions of wide Bayesian neural networks are described by a Gaussian process, known as the Neural Network Gaussian Process (NNGP). Analytic forms for NNGP kernels are known…
The geometry of integration in text classification RNNs
Kyle Aitken, Vinay V. Ramasesh, Ankush Garg +3
Despite the widespread application of recurrent neural networks (RNNs) across a variety of tasks, a unified understanding of how RNNs solve these tasks remains elusive. In particul…
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein +4
We show that the sum of the implicit generator log-density of a GAN with the logit score of the discriminator defines an energy function which yields the true data densi…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
Training Conditional Random Fields with Natural Gradient Descent
Yuan Cao
We propose a novel parameter estimation procedure that works efficiently for conditional random fields (CRF). This algorithm is an extension to the maximum likelihood estimation (M…