• “BLOOM: A 176B-Parameter Open-Access Multilingual Language Model”, BigScience Workshop, Arxiv Preprint.

  • “Named Tensor Notation”, David Chiang, Alexander M. Rush, Boaz Barak, TMLR 2022.

  • “Xatu: boosting existing DDoS detection systems using auxiliary signals”, Zhiying Xu, Sivaramakrishnan Ramanathan, Alexander Rush, Jelena Mirkovic, Minlan Yu, CoNEXT 2022.

  • “Unsupervised Text Deidentification”, John X Morris, Justin T Chiu, Ramin Zabih, Alexander M Rush, EMNLP Findings 2022.

  • “Model Criticism for Long-Form Text Generation”, Yuntian Deng, Volodymyr Kuleshov, Alexander M Rush, EMNLP 2022.

  • “Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurement”, Leandro von Werra et al., EMNLP Demos 2022 (Best Demo).

  • “Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models”, Hendik Strobelt et al., IEEE Trans on Visualization 2022.

  • “A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs”, Thierry Tambe et al., IEEE Solid-State Circuits 2022.

  • “Promptsource: An integrated development environment and repository for natural language prompts”, Stephen Bach et al., ACL Demo 2022.

  • “End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman”, Samantha Petti, et al., Bioinformatics.

  • “Multitask prompted training enables zero-shot task generalization”, Victor Sanh, et al., ICLR 2022.

  • “Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field”, Stanislav Lukyanenko et al., MICCAI 2021.

  • “Rationales for sequential predictions”, Keyon Vafa, Yuntian Deng, David Blei, Alexander Rush, EMNLP 2021.

  • “Low-Rank Constraints for Fast Inference in Structured Models”, Justin Chiu, Yuntian Deng, and Alexander M. Rush, NeurIPS 2021.

  • “Conference demographics and footprint changed by virtual platforms”, Matthe Skiles et al., Nature Sustainability.

  • “Sequence-to-Lattice Models for Fast Translation”, Yuntian Deng and Alexander M. Rush, EMNLP Findings Short 2021.

  • “Datasets: A Community Library for Natural Language Processing”, Quentin Lhoest et al, EMNLP Demos 2021 (Best Demo).

  • “EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference”, Thierry Tambe and Others, IEEE MICRO 2021.

  • “GenNI: Human-AI Collaboration for Data-Backed Text Generation”, Hendrik Strobelt, Jambay Kinley, Robert Krueger, Johanna Beyer, Alexander M. Rush, Hanspeter Pfister, IEEE VIS 2021.

  • “Parameter-efficient transfer learning with diff pruning”, Demi Guo, Alexander M. Rush, Yoon Kim, ACL 2021.

  • “How many data points is a prompt worth?”, Teven Le Scao, Alexander M. Rush, NAACL Short 2021 (Best Paper - Runner-Up).

  • “Block pruning for faster transformers”, François Lagunas, Ella Charlaix, Victor Sanh, Alexander M Rush, ACL 2021.

  • “Low-Complexity Probing via Finding Subnetworks”, Steven Cao, Victor Sanh, Alexander M. Rush, NAACL Short 2021.

  • “Template Filling with Generative Transformers”, Xinya Du, Alexander M. Rush, Claire Cardie, NAACL Short 2021.

  • “9.8 A 25mm2 SoC for IoT Devices with 18ms Noise-Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET”, Thierry Tambe, En-Yu Yang, Glenn G Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N Whatmough, Alexander M Rush, David Brooks, Gu-Yeon Wei, IEEE International Solid-State Circuits Conference 2021.

  • “Cascaded Text Generation with Markov Transformers”, Yuntian Deng, Alexander M. Rush, NeurIPS 2020.

  • “Latent Template Induction with Gumbel-CRFs”, Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng, Alexander Rush, NeurIPS 2020.

  • “Movement Pruning: Adaptive Sparsity by Fine-Tuning”, Victor Sanh, Thomas Wolf, Alexander M. Rush, NeurIPS 2020.

  • “Scaling Hidden Markov Language Models”, Justin T. Chiu, Alexander M. Rush, EMNLP 2020.

  • “Adversarial Semantic Collisions”, Congzheng Song, Alexander M. Rush, Vitaly Shmatikov, EMNLP 2020.

  • “Sequence-Level Mixed Sample Data Augmentation”, Demi Guo, Yoon Kim, Alexander M. Rush, EMNLP 2020.

  • “AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference”, Thierry Tambe, En-Yu Yang, Zishen Wan, Yuntian Deng, Vijay Janapa Reddi, Alexander Rush, David Brooks, Gu-Yeon Wei, DAC 2020 (Best Paper).

  • “Transformers: State-of-the-art Natural Language Processing”, Thomas Wolf et al, EMNLP Demos 2020 (Best Demo).

  • “Torch-Struct: Deep Structured Prediction Library”, Alexander Rush, ACL Demos 2020 (Best Demo Honorable Mention).

  • “What is Learned in Visually Grounded Neural Syntax Acquisition”, Noriyuki Kojima, Hadar Averbuch-Elor, Alexander M. Rush, Yoav Artzi, ACL 2020 (Short).

  • “Posterior Control of Blackbox Generation”, Xiang Lisa Li, Alexander M. Rush, ACL 2020.

  • “Automating Botnet Detection with Graph Neural Networks”, Jiawei Zhou, Zhiying Xu, Alexander M. Rush, Minlan Yu, AutoML for Networking and Systems Workshop.

  • “LAN – A materials notation for 2D layered assemblies”, Georgios A. Tritsaris, Yiqi Xie, Alexander M. Rush, Stephen Carr, Marios Mattheakis, Efthimios Kaxiras, .

  • “MASR: A Modular Accelerator for Sparse RNNs”, Udit Gupta, Brandon Reagen, Lillian Pentecost, Marco Donato, Thierry Tambe, Alexander M. Rush, Gu-Yeon Wei, David Brooks, PACT 2019.

  • “Commonsense Knowledge Mining from Pretrained Models”, Joe Davison, Joshua Feldman and Alexander Rush, EMNLP 2019.

  • “Neural Linguistic Steganography”, Zachary Ziegler, Yuntian Deng and Alexander Rush, EMNLP 2019.

  • “Compound Probabilistic Context-Free Grammars for Grammar Induction”, Yoon Kim, Chris Dyer, Alexander M. Rush, ACL 2019.

  • “Visual Interaction with Deep Learning Models through Collaborative Semantic Inference”, Gehrmann S, Strobelt H, Krueger R, Pfister H, and Alexander M. Rush, InfoVis 2019.

  • “Simple Unsupervised Summarization by Contextual Matching”, Jiawei Zhou, Alexander M. Rush, ACL 2019.

  • “GLTR: Statistical Detection and Visualization of Generated Text”, Sebastian Gehrmann, Hendrik Strobelt, Alexander M Rush, ACL Demo 2019 (Best Demo Honorable Mention).

  • “Unsupervised Recurrent Neural Network Grammars”, Yoon Kim, Alexander M. Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, Gabor Melis, NAACL 2019.

  • “Avoiding Latent Variable Collapse With Generative Skip Models”, Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei, AISTATS 2019.

  • “Tensor Variable Elimination for Plated Factor Graphs”, Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Justin Chiu, Neeraj Pradhan, Alexander Rush, Noah Goodman, ICML 2019.

  • “Latent Normalizing Flows for Discrete Sequences”, Zachary M. Ziegler, Alexander M. Rush, ICML 2019.

  • “Deep Latent-Variable Models for Natural Language”, Yoon Kim, Sam Wiseman, Alexander M. Rush, EMNLP 2018 (Tutorial).

  • “End-to-End Content and Plan Selection for Data-to-Text Generation”, Sebastian Gehrmann, Falcon Z. Dai, Henry Elder, Alexander M. Rush, INLG 2018.

  • “Latent Alignment and Variational Attention”, Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, Alexander M. Rush, NIPS 2018.

  • “Learning Neural Templates for Text Generation”, Sam Wiseman, Stuart M. Shieber, Alexander Rush, EMNLP 2018.

  • “Bottom-Up Abstractive Summarization”, Sebastian Gehrmann, Yuntian Deng, Alexander Rush, EMNLP 2018.

  • “Training for Diversity in Image Paragraph Captioning”, Luke Melas-Kyriazi, George Han, Alexander Rush, EMNLP 2018 (Short).

  • “Entity Tracking Improves Cloze-style Reading Comprehension”, Luong Hoang, Sam Wiseman, Alexander Rush, EMNLP 2018 (Short).

  • “Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models “, Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alexander M. Rush, VAST 2018, EMNLP-BlackBox 2018 (Best Paper - Honorable Mention).

  • “The Annotated Transformer”, Alexander M. Rush, ACL NLP-OSS 2018.

  • “OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU”, Jean Senellart, Dakun Zhang, Bo Wang, Guillaume Klein, J.P. Ramatchandirin, Josep Crego, Alexander M. Rush, WNMT 2018 (First-Place CPU Speed/Memory).

  • “Semi-Amortized Variational Autoencoders”, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush, ICML 2018.

  • “Compressing Deep Neural Networks with Probabilistic Data Structures”, Brandon Reagen, Udit Gupta, Robert Adolf, Michael M. Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David Brooks, ICML 2018, SysML 2018.

  • “Adapting Sequence Models for Sentence Correction”, Allen Schmaltz, Yoon Kim, Alexander M. Rush, Stuart M. Shieber, EMNLP 2017.

  • “Challenges in Data-to-Document Generation”, Sam Wiseman, Stuart M Shieber Alexander M. Rush, EMNLP 2017.

  • “Adversarially Regularized Autoencoders”, Junbo Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun, ICML 2018, NIPS 2017 Workshop.

  • “OpenNMT: Open-Source Toolkit for Neural Machine Translation”, Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush, ACL Demo 2017 (Best Demo Runner-up).

  • “Dilated Convolutions for Modeling Long-Distance Genomic Dependencies”, Ankit Gupta, Alexander M. Rush, ICML CompBio 2017 (Best Poster).

  • “Image-to-Markup Generation with Coarse-to-Fine Attention”, Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, and Alexander M. Rush, ICML 2017.

  • “LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks”, Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, and Alexander M. Rush, InfoVis 2017.

  • “Structured Attention Networks”, Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush, ICLR 2017.

  • “Lie-Access Neural Turing Machines”, Greg Yang and Alexander M. Rush, ICLR 2017.

  • “Sequence-Level Knowledge Distillation”, Yoon Kim and Alexander M. Rush, EMNLP 2016.

  • “Sequence-to-Sequence Learning as Beam-Search Optimization”, Sam Wiseman and Alexander M. Rush, EMNLP 2016 (Best Paper Runner-Up).

  • “An Embedding Model for Predicting Roll-Call Votes”, Peter Kraft, Hirsh Jain, and Alexander M. Rush, Proceedings of EMNLP 2016.

  • “Word Ordering Without Syntax”, Allen Schmaltz, Alexander M. Rush, and Stuart M. Shieber, EMNLP 2016.

  • “Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction”, Allen Schmaltz, Yoon Kim, Alexander M. Rush, and Stuart M. Shieber, Workshop Submission for AESW 2016 (Top Performing System).

  • “Learning Global Features for Coreference Resolution”, Sam Wiseman, Alexander M. Rush, and Stuart M. Shieber, NAACL 2016.

  • “Abstractive Sentence Summarization with Attentive Recurrent Neural Networks”, Sumit Chopra, Michael Auli, and Alexander M. Rush, NAACL 2016.

  • “Character-Aware Neural Language Models”, Yoon Kim, Yacine Jernite, David Sontag, and Alexander M. Rush, AAAI 2016.

  • “A Neural Attention Model for Abstractive Sentence Summarization”, Alexander M. Rush, Sumit Chopra, and Jason Weston, EMNLP 2015..

  • “Towards AI-Complete Question Answering A Set of Prerequisite Toy Tasks”, Jason Weston, Antoine Bordes, Sumit Chopra, Tomas Mikolov, and Alexander M. Rush, ArXiv Preprint.

  • “Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution”, Sam Wiseman, Alexander M. Rush, Jason Weston, and Stuart M. Shieber, ACL 2015..

  • “A Fast Variational Approach for Learning Markov Random Field Language Models”, Yacine Jernite, Alexander M. Rush, and David Sontag, ICML 2015..

  • “Transforming Dependencies into Phrase Structures”, Lingpeng Kong, Alexander M. Rush, and Noah A. Smith, NAACL 2015..