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

[PDF] [slides]

A Data Source for Reasoning Embodied Agents

Jack Lanchantin, Sainbayar Sukhbaatar, Gabriel Synnaeve, Yuxuan Sun, Kavya Srinet, Arthur Szlam

AAAI 2023  - Washington, DC

[PDF] [arXiv] [slides] [code]

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

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]