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Found 15 papers, 5 papers with code
High-Spatial Resolution Transmission and Storage Expansion Planning for High Renewable Grids: A Case Study
Transmission Expansion Planning (TEP) is the process of optimizing the development and upgrade of the power grid to ensure reliable, efficient, and cost-effective electricity delivery while addressing grid constraints.
FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?
There is great interest in fine-tuning frontier large language models (LLMs) to inject new information and update existing knowledge.
Regulating AI Adaptation: An Analysis of AI Medical Device Updates
In particular, the adaptive nature of AI models presents unique challenges to regulators as updating a model can improve its performance but also introduce safety risks.
ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence
Conversely, when the model's initial response is incorrect, does it always know to use the retrieved information to correct itself, or does it insist on its wrong prior response?
How well do LLMs cite relevant medical references? An evaluation framework and analyses
In this paper, we ask: do the sources that LLMs generate actually support the claims that they make?
Strong Mixed-Integer Formulations for Transmission Expansion Planning with FACTS Devices
Transmission Network Expansion Planning (TNEP) problems find the most economical way of expanding a given grid given long-term growth in generation capacity and demand patterns.
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline.
Explaining medical AI performance disparities across sites with confounder Shapley value analysis
Medical AI algorithms can often experience degraded performance when evaluated on previously unseen sites.
Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms
Breast cancer classification in mammography exemplifies these challenges, with a malignancy rate of around 0. 5% in a screening population, which is compounded by the relatively small size of lesions (~1% of the image) in malignant cases.
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018.
Validation of a deep learning mammography model in a population with low screening rates
We specifically explore how a deep learning algorithm trained on screening mammograms from the US and UK generalizes to mammograms collected at a hospital in China, where screening is not widely implemented.
Mixed Membership Recurrent Neural Networks
Models for sequential data such as the recurrent neural network (RNN) often implicitly model a sequence as having a fixed time interval between observations and do not account for group-level effects when multiple sequences are observed.
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition
We use a biologically inspired two-part convolutional neural network ('GistNet') that models the fovea and periphery to provide a proof-of-principle demonstration that computational object recognition can significantly benefit from the gist of the scene as contextual information.
