Search Results for author:
Found 10 papers, 1 papers with code
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks.
Mind's Eye: Grounded Language Model Reasoning through Simulation
By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world -- their failure to relate language to the physical world causes knowledge to be misrepresented and obvious mistakes in their reasoning.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Although chain-of-thought prompting has shown impressive results on many natural language reasoning tasks, it often performs poorly on tasks which need to solve problems harder than the demonstration examples.
Ranked #14 on
Arithmetic Reasoning
on GSM8K
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #17 on
Code Generation
on HumanEval
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Scaling language models with more data, compute and parameters has driven significant progress in natural language processing.
Ranked #5 on
Common Sense Reasoning
on ARC (Easy)
(using extra training data)
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.
Deep Physiological State Space Model for Clinical Forecasting
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements.
Modelling EHR timeseries by restricting feature interaction
Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests.
Explaining an increase in predicted risk for clinical alerts
We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.
