2.1k citations · 6.8k across the 40 of their papers we have counts for
16 papers · 1 filter
Deep State-Space Generative Model For Correlated Time-to-Event Predictions
Yuan Xue, Denny Zhou, Nan Du +4
Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning.…
Large Language Models as Optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu +4
Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applicati…
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan +3
In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning. The common practice of usi…
Large Language Models as Tool Makers
Tianle Cai, Xuezhi Wang, Tengyu Ma +2
Recent research has highlighted the potential of large language models (LLMs) to improve their problem-solving capabilities with the aid of suitable external tools. In our work, we…
What learning algorithm is in-context learning? Investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas +2
Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning. They can construct new predictors from sequences of labeled examples $(x, f(…
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre +32
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we expl…