ToolVerifier: Generalization to New Tools via Self-Verification
Dheeraj Mekala, Jason Weston, Jack Lanchantin, Roberta Raileanu, Maria Lomeli, Jingbo Shang, Jane Dwivedi-Yu
arXiv 2024
[PDF] [arXiv] [slides]
Learning to Reason and Memorize with Self-Notes
Jack Lanchantin*, Shubham Toshniwal*, Jason Weston, Arthur Szlam, Sainbayar Sukhbaatar
*equal contribution
NeurIPS 2023 - New Orleans, LA
[PDF] [arXiv] [slides]
Robustness of Named-Entity Replacements for In-Context Learning
Saeed Goodarzi, Nikhil Kagita, Dennis Minn, Shufan Wang, Roberto Dessi, Shubham Toshniwal, Adina Williams, Jack Lanchantin*, Koustuv Sinha*
*equal leadership
EMNLP Findings 2023 - Singapore
Compositional Interfaces for Compositional Generalization
Jelena Luketina, Jack Lanchantin, Sainbayar Sukhbaatar, Arthur Szlam
ES-FoMo ICML Workshop 2023 - Honolulu, HI
A Data Source for Reasoning Embodied Agents
Jack Lanchantin, Sainbayar Sukhbaatar, Gabriel Synnaeve, Yuxuan Sun, Kavya Srinet, Arthur Szlam
AAAI 2023 - Washington, DC
Modeling interactions with Deep Learning
Jack Lanchantin
PhD Dissertation - 2021
Committee: Vicente Ordoñez (chair), Yangfeng Ji, Clint Miller, Casey Greene, Yanjun Qi
General Multi-label Image Classification with Transformers
Jack Lanchantin, Tianlu Wang, Vicente Ordóñez Román, Yanjun Qi
Conference on Computer Vision and Pattern Recognition (CVPR) 2021 - Nashville, TN
[PDF] [arXiv] [poster] [slides] [video] [code]
Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences
Jack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, Yanjun Qi
ACM-BCB 2021, NeurIPS Covid-19 Symposium 2020, Machine Learning in Computational Biology (MLCB) 2020
[PDF] [bioRxiv] [slides] [video] [code]
Time and Space Complexity of Graph Convolutional Networks
Derrick Blakely, Jack Lanchantin, Yanjun Qi
Tech Report 2021
[PDF]
Graph Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data
Jack Lanchantin, Yanjun Qi
European Conference on Computational Biology (ECCB) 2020, Bioinformatics 2020
[PDF] [bioRxiv] [slides] [poster] [code]
Reevaluating Adversarial Examples in Natural Language
John X. Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji, Yanjun Qi
Findings of the Association for Computational Linguistics - EMNLP 2020
[PDF] [arXiv] [slides] [code]
FastSK: Fast Sequence Analysis with Gapped String Kernels
Derrick Blakely, Eamon Collins, Ritambhara Singh, Andrew Norton, Jack Lanchantin, Yanjun Qi
Bioinformatics 2020
[PDF] [Bioinformatics] [code]
Neural Message Passing for Multi-Label Classification
Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
European Conference on Machine Learning (ECML-PKDD) 2019 - Würzburg, Germany
[PDF] [arXiv] [slides] [poster] [code]
Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, Yanjun Qi
Deep Learning and Security Workshop (DLS) 2018 - San Francisco, CA
[PDF] [arXiv] [slides] [video] [code]
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
Advances in Neural Information Processing Systems (NeurIPS) 2017 - Long Beach, CA
[PDF] [arXiv] [slides] [code] [Kipoi] [poster]
Opportunities and Obstacles for Deep Learning in Biology and Medicine
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Wei Xie, Gail L Rosen, Benjamin J Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M Cofer, David J Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K Wiley, Marwin HS Segler, Anthony Gitter, Casey S Greene
Journal of the Royal Society Interface 2018
[PDF] [JRSI] [Nature Tech Blog]
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
Jack Lanchantin, Arshdeep Sekhon, Ritambhara Singh, Yanjun Qi
arXiv Preprint 2017
[PDF] [arXiv] [slides]
Memory Matching Networks for Genomic Sequence Classification
Jack Lanchantin, Ritambhara Singh, Yanjun Qi
International Conference on Learning Representations (ICLR) Workshop Track 2017 - Toulon, France
[PDF] [arXiv] [poster]
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi
Pacific Symposium on Biocomputing (PSB) 2017 - Kohala Coast, HI
[PDF] [arXiv] [slides] [code] [poster]
Deep Motif: Visualizing Genomic Sequence Classifications
Jack Lanchantin, Ritambhara Singh, Zeming Lin, Yanjun Qi
International Conference on Learning Representations (ICLR) Workshop Track 2016 - San Juan, PR
[PDF] [arXiv] [code] [poster]
DeepChrome: Deep Learning for Predicting Gene Expression from Histone Modifications
Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi
European Conference on Computational Biology (ECCB) 2016 - The Hague, Netherlands
[PDF] [arXiv] [slides] [code]
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 2016
[PDF] [arXiv] [slides] [code]
Exploring the Naturalness of Code with Recurrent Neural Nets
Jack Lanchantin, Ji Gao
arXiv Preprint 2016
[PDF] [arXiv] [slides] [code]
MUST-CNN: A Multilayer Shift-and-Stitch Convolutional Architecture for Sequence-based Protein Structure Prediction
Zeming Lin, Jack Lanchantin, Yanjun Qi
The 30th AAAI Conference on Artificial Intelligence (AAAI) 2016 - Phoenix, AZ
[PDF] [arXiv] [slides] [code]
Scene Labeling with Convolutional Neural Nets
Zeming Lin, Jack Lanchantin
Preprint 2015
[PDF] [slides] [code]