Language models, such as [1, 2, 3] have emerged to be an efficient tool in the domain of EHR tasks. These models, extensively trained on diverse sources of clinical data, such as physician notes and longitudinal medical codes, have demonstrated remarkable effectiveness in predicting clinical outcomes. Despite their capabilities, measuring and reducing the uncertainties of these models in EHR tasks is crucial for ensuring patient safety, as clinicians can avoid interventions that the model indicates are uncertain and potentially hazardous. In addition, quantifying the uncertainties in clinical tasks can enhance the reliability of AI-driven medical decision-making systems [4]. To address this challenge, leveraging the transparency of model parameters, we utilize established uncertainty metrics and propose to combine them with ensembling and multi-tasking approaches to effectively quantify and mitigate uncertainties in EHR tasks for these white-box language models. Recently, large language models have embarked on demonstrating their utility in clinical-related tasks, including EHR prediction tasks [5], analyzing radiology report examinations [6] and medical reasoning [7]. However, the encapsulation of modern Large Language Models, typically offered as API services with restricted access to internal model parameters and prediction probabilities, impedes the direct application of traditional uncertainty quantification methods. To overcome this limitation, We redefine uncertainty quantification as a post-hoc approach by analyzing the distribution of answers generated repeatedly from our designed prompts for clinical prediction tasks. Inspired by the effectiveness of our proposed methods in reducing model uncertainty for white-box LMs, we adapted and applied ensembling and multi-tasking methods to the black-box settings.

In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks. Our investigation reveals that while GPT-4V exhibits proficiency in discerning the position and interrelations of 2D entities through current visual prompting techniques, its abilities in handling 3D spatial tasks have yet to be explored. In our approach, we create a 3D coordinate system tailored to 3D imagery, complete with annotated scale information. By presenting images infused with the 3DAP visual prompt as inputs, we empower GPT-4V to ascertain the spatial positioning information of the given 3D target image with a high degree of precision. Through experiments, We identified three tasks that could be stably completed using the 3DAP method, namely, 2D to 3D Point Reconstruction, 2D to 3D point matching, and 3D Object Detection. We perform experiments on our proposed dataset 3DAP-Data, the results from these experiments validate the efficacy of 3DAP-enhanced GPT-4V inputs, marking a significant stride in 3D spatial task execution.

It is no secret amongst deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between state-of-the-art performance and a run-of-the-mill result. To that end, the community has seen many efforts to automate the process of finding the perfect augmentation procedure for any task at hand. Unfortunately, even recent cutting-edge methods bring massive computational overhead, requiring as many as 100 full model trainings to settle on an ideal configuration. We show how to achieve equivalent performance using just 6 trainings with Random Unidimensional Augmentation. Source code is available at https://github.com/fastestimator/RUA/tree/v1.0

There is a parameter ubiquitous throughout the deep learning world: learning rate. There is likewise a ubiquitous question: what should that learning rate be? The true answer to this question is often tedious and time consuming to obtain, and a great deal of arcane knowledge has accumulated in recent years over how to pick and modify learning rates to achieve optimal training performance. Moreover, the long hours spent carefully crafting the perfect learning rate can come to nothing the moment your network architecture, optimizer, dataset, or initial conditions change ever so slightly. But it need not be this way. We propose a new answer to the great learning rate question: the Autonomous Learning Rate Controller. Find it at https://github.com/fastestimator/ARC/tree/v2.0