63 citations · 121 across the 15 of their papers we have counts for
15 papers
Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives
Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6
Materials science data collection can be expensive, making dataset reuse and long-term utility important for future discovery. In practice, data collection often prioritizes proper…
Vendi Novelty Scores for Out-of-Distribution Detection
Amey P. Pasarkar, Adji Bousso Dieng
Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems. Existing post-hoc detectors typically rely on model confidence scores or likeli…
Are neural scaling laws leading quantum chemistry astray?
Siwoo Lee, Adji Bousso Dieng
Neural scaling laws are driving the machine learning community toward training ever-larger foundation models across domains, assuring high accuracy and transferable representations…
Applications of the Vendi score in genomic epidemiology
Bjarke Frost Nielsen, Amey P. Pasarkar, Qiqi Yang +2
The Vendi score (VS), a diversity metric recently conceived in the context of machine learning, with applications in a wide range of fields, has a few distinct advantages over the…
A Unified and Predictive Measure of Functional Diversity
Adji Bousso Dieng, Amey Pasarkar
Despite the critical role of functional diversity (FD) in understanding ecological systems and processes, its robust quantification remains a significant challenge. A long-held vie…
Rethinking Ecological Measures Of Functional Diversity
Ines Meraoumia, Adji Bousso Dieng
Understanding functional diversity, the range and variability of species' roles and actions within their communities, is key to predicting and preserving the functions that sustain…
MuAPBEK: An Improved Analytical Kinetic Energy Density Functional for Quantum Chemistry
Siwoo Lee, Adji Bousso Dieng
Orbital-free density functional theory (OFDFT) offers a true realization of the Hohenberg-Kohn theorems, enabling full quantum-mechanical studies of electronic systems based solely…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
The Vendiscope: An Algorithmic Microscope For Data Collections
Amey P. Pasarkar, Adji Bousso Dieng
The evolution of microscopy, beginning with its invention in the late 16th century, has continuously enhanced our ability to explore and understand the microscopic world, enabling…
Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs
Mohammad Reza Rezaei, Adji Bousso Dieng
Retrieval-augmented generation (RAG) enhances large language models (LLMs) for domain-specific question-answering (QA) tasks by leveraging external knowledge sources. However, trad…
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers +1
Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based ma…
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei +5
Scientific machine learning (ML) endeavors to develop generalizable models with broad applicability. However, the assessment of generalizability is often based on heuristics. Here,…
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions
Andre Niyongabo Rubungo, Craig Arnold, Barry P. Rand +1
The prediction of crystal properties plays a crucial role in the crystal design process. Current methods for predicting crystal properties focus on modeling crystal structures usin…
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
Kyurae Kim, Jisu Oh, Jacob R. Gardner +2
Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior. Rec…
Variational Inference via -Upper Bound Minimization
Adji B. Dieng, Dustin Tran, Rajesh Ranganath +2
Variational inference (VI) is widely used as an efficient alternative to Markov chain Monte Carlo. It posits a family of approximating distributions and finds the closest membe…