Are Learned Molecular Representations Ready For Prime Time? - Kevin Yang; Kyle Swanson; Wengong Jin; Connor W. Coley; Philipp Eiden; Hua Gao; Angel Guzman-Perez; Timothy Hopper; Brian Kelley; Miriam Mathea; Andrew Palmer; Volker Settels; et al. (CoRR, 2019)

Building a Production Model for Retrieval-Based Chatbots. - Kyle Swanson; Lili Yu; Christopher Fox; Jeremy Wohlwend; Tao Lei 001 (CoRR, 2019)

Deep Learning for Automated Classification and Characterization of Amorphous Materials. - Kirk Swanson; Shubhendu Trivedi; Joshua Lequieu; Kyle Swanson; Risi Kondor (CoRR, 2019)

Analyzing Learned Molecular Representations for Property Prediction. - Kevin Yang; Kyle Swanson; Wengong Jin; Connor W. Coley; Philipp Eiden; Hua Gao; Angel Guzman-Perez; Timothy Hopper; Brian Kelley; Miriam Mathea; Andrew Palmer; Volker Settels; et al. (J. Chem. Inf. Model., 2019)

Correction to Analyzing Learned Molecular Representations for Property Prediction. - Kevin Yang; Kyle Swanson; Wengong Jin; Connor W. Coley; Philipp Eiden; Hua Gao; Angel Guzman-Perez; Timothy Hopper; Brian Kelley; Miriam Mathea; Andrew Palmer; Volker Settels; et al. (J. Chem. Inf. Model., 2019)

Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport. - Kyle Swanson; Lili Yu; Tao Lei 001 (ACL, 2020)

Improving Molecular Design by Stochastic Iterative Target Augmentation. - Kevin Yang; Wengong Jin; Kyle Swanson; Regina Barzilay; Tommi S. Jaakkola (ICML, 2020)

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction. - Lior Hirschfeld; Kyle Swanson; Kevin Yang; Regina Barzilay; Connor W. Coley (J. Chem. Inf. Model., 2020)

VMAF-based Bitrate Ladder Estimation for Adaptive Streaming. - Angeliki V. Katsenou; Fan Zhang 017; Kyle Swanson; Mariana Afonso; Joel Sole; David R. Bull (PCS, 2021)

Monte Carlo Tree Search for Interpreting Stress in Natural Language. - Kyle Swanson; Joy Hsu; Mirac Suzgun (LT-EDI, 2022)

Predicting Immune Escape with Pretrained Protein Language Model Embeddings. - Kyle Swanson; Howard Chang; James Zou 001 (MLCB, 2022)

Predicting Drug Solubility Using Different Machine Learning Methods - Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network. - John Ho; Zhao-Heng Yin; Colin Zhang; Henry Overhauser; Kyle Swanson; Yang Ha (CoRR, 2023)

Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN. - Yanay Rosen; Yusuf H. Roohani; Maria Brbic; Kyle Swanson; Ziang Li; Jure Leskovec (Zenodo, 2023)

UniTox: Leveraging LLMs to Curate a Unified Dataset of Drug-Induced Toxicity from FDA Labels. - Jacob Silberg; Kyle Swanson; Elana Simon; Angela Zhang; Zaniar Ghazizadeh; Scott Ogden; Hisham Hamadeh; James Y. Zou (NeurIPS, 2024)

ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. - Kyle Swanson; Parker Walther; Jeremy Leitz; Souhrid Mukherjee; Joseph C. Wu; Rabindra V. Shivnaraine; James Zou 001 (Bioinform., 2024)

Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. - Kyle Swanson; Gary Liu; Denise B. Catacutan; Autumn Arnold; James Zou 001; Jonathan M. Stokes (Nat. Mac. Intell., 2024)