1.6k citations · 1.9k across the 32 of their papers we have counts for
10 papers · 1 filter
Efficient RLHF: Reducing the Memory Usage of PPO
Michael Santacroce, Yadong Lu, Han Yu +2
Reinforcement Learning with Human Feedback (RLHF) has revolutionized language modeling by aligning models with human preferences. However, the RL stage, Proximal Policy Optimizatio…
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks
Yuan Cao, Difan Zou, Yuanzhi Li +1
We study the implicit bias of batch normalization trained by gradient descent. We show that when learning a linear model with batch normalization for binary classification, gradien…
Length Generalization in Arithmetic Transformers
Samy Jelassi, Stéphane d'Ascoli, Carles Domingo-Enrich +3
We examine how transformers cope with two challenges: learning basic integer arithmetic, and generalizing to longer sequences than seen during training. We find that relative posit…
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
Ming Jin, Guangsi Shi, Yuan-Fang Li +7
Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural ne…
On the Importance of Contrastive Loss in Multimodal Learning
Yunwei Ren, Yuanzhi Li
Recently, contrastive learning approaches (e.g., CLIP (Radford et al., 2021)) have received huge success in multimodal learning, where the model tries to minimize the distance betw…
The Benefits of Mixup for Feature Learning
Difan Zou, Yuan Cao, Yuanzhi Li +1
Mixup, a simple data augmentation method that randomly mixes two data points via linear interpolation, has been extensively applied in various deep learning applications to gain be…
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
Yuchen Li, Yuanzhi Li, Andrej Risteski
While the successes of transformers across many domains are indisputable, accurate understanding of the learning mechanics is still largely lacking. Their capabilities have been pr…
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Yue Wu, Yewen Fan, Paul Pu Liang +3
High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructu…
Towards Understanding Mixture of Experts in Deep Learning
Zixiang Chen, Yihe Deng, Yue Wu +2
The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a router, has achieved great success in deep learning. However, the understanding of such architecture…
Boosting House Price Predictions using Geo-Spatial Network Embedding
Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali, Yuan-Fang Li +2
Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financ…