Sample Selection for Maximal Diversity. - Feng Pan 001; Adam Roberts; Leonard McMillan; David Threadgill; Wei Wang 010 (ICDM, 2007)

Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows. - Adam Roberts; Leonard McMillan; Wei Wang 010; Joel Parker; Ivan Rusyn; David Threadgill (ISMB/ECCB (Supplement of Bioinformatics), 2007)

Automated Risk Detection - What are the Key Elements Needed to Create a Multi-source, Pattern-based Risk Detection System? - Ian Blunt; Xavier Chitnis; Adam Roberts (HEALTHINF, 2009)

Fast Statistical Alignment. - Robert K. Bradley; Adam Roberts; Michael E. Smoot; Sudeep Juvekar; Jaeyoung Do; Colin N. Dewey; Ian H. Holmes; Lior Pachter (PLoS Comput. Biol., 2009)

Identification of novel transcripts in annotated genomes using RNA-Seq. - Adam Roberts; Harold Pimentel; Cole Trapnell; Lior Pachter (Bioinform., 2011)

Quantifying uniformity of mapped reads. - Valerie Hower; Richard Starfield; Adam Roberts; Lior Pachter (Bioinform., 2012)

Updating RNA-Seq analyses after re-annotation. - Adam Roberts; Lorian Schaeffer; Lior Pachter (Bioinform., 2013)

Fragment assignment in the cloud with eXpress-D. - Adam Roberts; Harvey Feng; Lior Pachter (BMC Bioinform., 2013)

Ambiguous fragment assignment for high-throughput sequencing experiments. - Adam Roberts (2013)

Perception and representation of Bengali nasal vowels. - Sandra Kotzor; Adam Roberts; Allison Wetterlin; Aditi Lahiri (ICPhS, 2015)

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders. - Jesse H. Engel; Cinjon Resnick; Adam Roberts; Sander Dieleman; Mohammad Norouzi 002; Douglas Eck; Karen Simonyan (ICML, 2017)

Counterpoint by Convolution. - Cheng-Zhi Anna Huang; Tim Cooijmans; Adam Roberts; Aaron C. Courville; Douglas Eck (ISMIR, 2017)

Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models. - Jesse H. Engel; Matthew D. Hoffman; Adam Roberts (ICLR (Poster), 2018)

A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music. - Adam Roberts; Jesse H. Engel; Colin Raffel; Curtis Hawthorne; Douglas Eck (ICML, 2018)

Onsets and Frames: Dual-Objective Piano Transcription. - Curtis Hawthorne; Erich Elsen; Jialin Song; Adam Roberts; Ian Simon; Colin Raffel; Jesse H. Engel; Sageev Oore; Douglas Eck (ISMIR, 2018)

Learning Latent Representations of Music to Generate Interactive Musical Palettes. - Adam Roberts; Jesse H. Engel; Sageev Oore; Douglas Eck (IUI Workshops, 2018)

Learning a Latent Space of Multitrack Measures. - Ian Simon; Adam Roberts; Colin Raffel; Jesse H. Engel; Curtis Hawthorne; Douglas Eck (CoRR, 2018)

Identifying the Intersections: User Experience + Research Scientist Collaboration in a Generative Machine Learning Interface. - Claire Kayacik; Sherol Chen; Signe Noerly; Jess Holbrook; Adam Roberts; Douglas Eck (CHI Extended Abstracts, 2019)

GANSynth: Adversarial Neural Audio Synthesis. - Jesse H. Engel; Kumar Krishna Agrawal; Shuo Chen; Ishaan Gulrajani; Chris Donahue; Adam Roberts (ICLR (Poster), 2019)

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset. - Curtis Hawthorne; Andriy Stasyuk; Adam Roberts; Ian Simon; Cheng-Zhi Anna Huang; Sander Dieleman; Erich Elsen; Jesse H. Engel; Douglas Eck (ICLR, 2019)

Learning to Groove with Inverse Sequence Transformations. - Jon Gillick; Adam Roberts; Jesse H. Engel; Douglas Eck; David Bamman (ICML, 2019)

Fast and Flexible Neural Audio Synthesis. - Lamtharn Hantrakul; Jesse H. Engel; Adam Roberts; Chenjie Gu (ISMIR, 2019)

Approachable Music Composition with Machine Learning at Scale. - Cheng-Zhi Anna Huang; Curtis Hawthorne; Adam Roberts; Monica Dinculescu; James Wexler; Leon Hong; Jacob Howcroft (ISMIR, 2019)

The Bach Doodle: Approachable music composition with machine learning at scale. - Cheng-Zhi Anna Huang; Curtis Hawthorne; Adam Roberts; Monica Dinculescu; James Wexler; Leon Hong; Jacob Howcroft (CoRR, 2019)

How Much Knowledge Can You Pack Into the Parameters of a Language Model? - Adam Roberts; Colin Raffel; Noam Shazeer (EMNLP (1), 2020)

DDSP: Differentiable Digital Signal Processing. - Jesse H. Engel; Lamtharn Hantrakul; Chenjie Gu; Adam Roberts (ICLR, 2020)

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned. - Sewon Min; Jordan L. Boyd-Graber; Chris Alberti; Danqi Chen 001; Eunsol Choi; Michael Collins 001; Kelvin Guu; Hannaneh Hajishirzi; Kenton Lee; Jennimaria Palomaki; Colin Raffel; Adam Roberts; et al. (NeurIPS (Competition and Demos), 2020)

WT5?! Training Text-to-Text Models to Explain their Predictions. - Sharan Narang; Colin Raffel; Katherine Lee; Adam Roberts; Noah Fiedel; Karishma Malkan (CoRR, 2020)

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. - Colin Raffel; Noam Shazeer; Adam Roberts; Katherine Lee; Sharan Narang; Michael Matena; Yanqi Zhou; Wei Li 133; Peter J. Liu (J. Mach. Learn. Res., 2020)

Do Transformer Modifications Transfer Across Implementations and Applications? - Sharan Narang; Hyung Won Chung; Yi Tay; Liam Fedus; Thibault Févry; Michael Matena; Karishma Malkan; Noah Fiedel; Noam Shazeer; Zhenzhong Lan; Yanqi Zhou; Wei Li 133; et al. (EMNLP (1), 2021)

mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer. - Linting Xue; Noah Constant; Adam Roberts; Mihir Kale; Rami Al-Rfou; Aditya Siddhant; Aditya Barua; Colin Raffel (NAACL-HLT, 2021)

Extracting Training Data from Large Language Models. - Nicholas Carlini; Florian Tramèr; Eric Wallace; Matthew Jagielski; Ariel Herbert-Voss; Katherine Lee; Adam Roberts; Tom B. Brown; Dawn Song; Úlfar Erlingsson; Alina Oprea; Colin Raffel (USENIX Security Symposium, 2021)

What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization? - Thomas Wang; Adam Roberts; Daniel Hesslow; Teven Le Scao; Hyung Won Chung; Iz Beltagy; Julien Launay; Colin Raffel (ICML, 2022)

Multi-instrument Music Synthesis with Spectrogram Diffusion. - Curtis Hawthorne; Ian Simon; Adam Roberts; Neil Zeghidour; Josh Gardner 001; Ethan Manilow; Jesse H. Engel (ISMIR, 2022)

LaMDA: Language Models for Dialog Applications. - Romal Thoppilan; Daniel De Freitas; Jamie Hall; Noam Shazeer; Apoorv Kulshreshtha; Heng-Tze Cheng; Alicia Jin; Taylor Bos; Leslie Baker; Yu Du; YaGuang Li; Hongrae Lee; et al. (CoRR, 2022)

Scaling Up Models and Data with t5x and seqio. - Adam Roberts; Hyung Won Chung; Anselm Levskaya; Gaurav Mishra; James Bradbury; Daniel Andor; Sharan Narang; Brian Lester; Colin Gaffney; Afroz Mohiuddin; Curtis Hawthorne; Aitor Lewkowycz; et al. (CoRR, 2022)

What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? - Thomas Wang; Adam Roberts; Daniel Hesslow; Teven Le Scao; Hyung Won Chung; Iz Beltagy; Julien Launay; Colin Raffel (CoRR, 2022)

Scaling Instruction-Finetuned Language Models. - Hyung Won Chung; Le Hou; Shayne Longpre; Barret Zoph; Yi Tay; William Fedus; Eric Li; Xuezhi Wang 002; Mostafa Dehghani 001; Siddhartha Brahma; Albert Webson; Shixiang Shane Gu; et al. (CoRR, 2022)

VeLO: Training Versatile Learned Optimizers by Scaling Up. - Luke Metz; James Harrison; C. Daniel Freeman; Amil Merchant; Lucas Beyer; James Bradbury; Naman Agrawal; Ben Poole; Igor Mordatch; Adam Roberts; Jascha Sohl-Dickstein (CoRR, 2022)

ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models. - Linting Xue; Aditya Barua; Noah Constant; Rami Al-Rfou; Sharan Narang; Mihir Kale; Adam Roberts; Colin Raffel (Trans. Assoc. Comput. Linguistics, 2022)

Character-Aware Models Improve Visual Text Rendering. - Rosanne Liu; Dan Garrette; Chitwan Saharia; William Chan; Adam Roberts; Sharan Narang; Irina Blok; RJ Mical; Mohammad Norouzi 002; Noah Constant (ACL (1), 2023)

Crosslingual Generalization through Multitask Finetuning. - Niklas Muennighoff; Thomas Wang; Lintang Sutawika; Adam Roberts; Stella Biderman; Teven Le Scao; M. Saiful Bari; Sheng Shen 001; Zheng Xin Yong; Hailey Schoelkopf; Xiangru Tang; Dragomir Radev; et al. (ACL (1), 2023)

UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining. - Hyung Won Chung; Xavier Garcia; Adam Roberts; Yi Tay; Orhan Firat; Sharan Narang; Noah Constant (ICLR, 2023)

Large Language Models Struggle to Learn Long-Tail Knowledge. - Nikhil Kandpal; Haikang Deng; Adam Roberts; Eric Wallace; Colin Raffel (ICML, 2023)

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. - Shayne Longpre; Le Hou; Tu Vu; Albert Webson; Hyung Won Chung; Yi Tay; Denny Zhou; Quoc V. Le; Barret Zoph; Jason Wei; Adam Roberts (ICML, 2023)

MusicLM: Generating Music From Text. - Andrea Agostinelli; Timo I. Denk; Zalán Borsos; Jesse H. Engel; Mauro Verzetti; Antoine Caillon; Qingqing Huang; Aren Jansen; Adam Roberts; Marco Tagliasacchi; Matthew Sharifi; Neil Zeghidour; et al. (CoRR, 2023)

SingSong: Generating musical accompaniments from singing. - Chris Donahue; Antoine Caillon; Adam Roberts; Ethan Manilow; Philippe Esling; Andrea Agostinelli; Mauro Verzetti; Ian Simon; Olivier Pietquin; Neil Zeghidour; Jesse H. Engel (CoRR, 2023)

Report of the 1st Workshop on Generative AI and Law. - A. Feder Cooper; Katherine Lee; James Grimmelmann; Daphne Ippolito; Christopher Callison-Burch; Christopher A. Choquette-Choo; Niloofar Mireshghallah; Miles Brundage; David Mimno; Madiha Zahrah Choksi; Jack M. Balkin; Nicholas Carlini; et al. (CoRR, 2023)

PaLM: Scaling Language Modeling with Pathways. - Aakanksha Chowdhery; Sharan Narang; Jacob Devlin; Maarten Bosma; Gaurav Mishra; Adam Roberts; Paul Barham 001; Hyung Won Chung; Charles Sutton; Sebastian Gehrmann; Parker Schuh; Kensen Shi; et al. (J. Mach. Learn. Res., 2023)

Scaling Up Models and Data with t5x and seqio. - Adam Roberts; Hyung Won Chung; Gaurav Mishra; Anselm Levskaya; James Bradbury; Daniel Andor; Sharan Narang; Brian Lester; Colin Gaffney; Afroz Mohiuddin; Curtis Hawthorne; Aitor Lewkowycz; et al. (J. Mach. Learn. Res., 2023)

Adaptive Accompaniment with ReaLchords. - Yusong Wu; Tim Cooijmans; Kyle Kastner; Adam Roberts; Ian Simon; Alexander Scarlatos; Chris Donahue; Cassie Tarakajian; Shayegan Omidshafiei; Aaron C. Courville; Pablo Samuel Castro; Natasha Jaques; et al. (ICML, 2024)

A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity. - Shayne Longpre; Gregory Yauney; Emily Reif; Katherine Lee; Adam Roberts; Barret Zoph; Denny Zhou; Jason Wei; Kevin Robinson; David Mimno; Daphne Ippolito (NAACL-HLT, 2024)

Gemma: Open Models Based on Gemini Research and Technology. - Thomas Mesnard; Cassidy Hardin; Robert Dadashi; Surya Bhupatiraju; Shreya Pathak; Laurent Sifre; Morgane Rivière; Mihir Sanjay Kale; Juliette Love; Pouya Tafti; Léonard Hussenot; Aakanksha Chowdhery; et al. (CoRR, 2024)

Scaling Instruction-Finetuned Language Models. - Hyung Won Chung; Le Hou; Shayne Longpre; Barret Zoph; Yi Tay; William Fedus; Yunxuan Li; Xuezhi Wang 002; Mostafa Dehghani 001; Siddhartha Brahma; Albert Webson; Shixiang Shane Gu; et al. (J. Mach. Learn. Res., 2024)

Training LLMs over Neurally Compressed Text. - Brian Lester; Jaehoon Lee 001; Alexander A. Alemi; Jeffrey Pennington; Adam Roberts; Jascha Sohl-Dickstein; Noah Constant (Trans. Mach. Learn. Res., 2024)

ReaLJam: Real-Time Human-AI Music Jamming with Reinforcement Learning-Tuned Transformers. - Alexander Scarlatos; Yusong Wu; Ian Simon; Adam Roberts; Tim Cooijmans; Natasha Jaques; Cassie Tarakajian; Cheng-Zhi Anna Huang (CHI Extended Abstracts, 2025)

Live Music Models. - Antoine Caillon; Brian McWilliams; Cassie Tarakajian; Ian Simon; Ilaria Manco; Jesse H. Engel; Noah Constant; Yunpeng Li 008; Timo I. Denk; Alberto Lalama; Andrea Agostinelli; Cheng-Zhi Anna Huang; et al. (CoRR, 2025)