Cody Blakeney

19 papers A* 1A 1Journal 15Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
DatBench: Discriminative, Faithful, and Efficient VLM Evaluations.
CoRR
Siddharth Joshi, Haoli Yin, Rishabh Adiga, Ricardo Monti, Aldo Carranza, Alex Fang, Alvin Deng, Amro Abbas, Brett W. Larsen, Cody Blakeney, Darren Teh, David Schwab, Fan Pan, Haakon Mongstad, Jack Urbanek, Jason Lee, Jason Telanoff, Josh Wills, Kaleigh Mentzer, Luke Merrick, Parth Doshi, Paul Burstein, Pratyush Maini, Scott Loftin, Spandan Das, Tony Jiang, Vineeth Dorna, Zhengping Wang, Bogdan Gaza, Ari Morcos, Matthew L. Leavitt
2026 J jnl
The Finetuner's Fallacy: When to Pretrain with Your Finetuning Data.
CoRR
Christina Baek, Ricardo Pio Monti, David Schwab, Amro Abbas, Rishabh Adiga, Cody Blakeney, Maximilian Böther, Paul Burstein, Aldo Gael Carranza, Alvin Deng, Parth Doshi, Vineeth Dorna, Alex Fang, Tony Jiang, Siddharth Joshi, Brett W. Larsen, Jason Chan Lee, Katherine L. Mentzer, Luke Merrick, Haakon Mongstad, Fan Pan, Anshuman Suri, Darren Teh, Jason Telanoff, Jack Urbanek, Zhengping Wang, Josh Wills, Haoli Yin, Aditi Raghunathan, J. Zico Kolter, Bogdan Gaza, Ari Morcos, Matthew L. Leavitt, Pratyush Maini
2026 J jnl
ÜberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset.
CoRR
Aldo Gael Carranza, Kaleigh Mentzer, Ricardo Pio Monti, Alex Fang, Alvin Deng, Amro Abbas, Anshuman Suri, Brett W. Larsen, Cody Blakeney, Darren Teh, David Schwab, Diego Kiner, Fan Pan, Haakon Mongstad, Haoli Yin, Jack Urbanek, Jason Lee, Jason Telanoff, Josh Wills, Luke Merrick, Maximilian Böther, Parth Doshi, Paul Burstein, Pratyush Maini, Rishabh Adiga, Siddharth Joshi, Spandan Das, Tony Jiang, Vineeth Dorna, Zhengping Wang, Bogdan Gaza, Ari Morcos, Matthew L. Leavitt
2025 J jnl
BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining.
CoRR
Pratyush Maini, Vineeth Dorna, Parth Doshi, Aldo Carranza, Fan Pan, Jack Urbanek, Paul Burstein, Alex Fang, Alvin Deng, Amro Abbas, Brett W. Larsen, Cody Blakeney, Charvi Bannur, Christina Baek, Darren Teh, David Schwab, Haakon Mongstad, Haoli Yin, Josh Wills, Kaleigh Mentzer, Luke Merrick, Ricardo Monti, Rishabh Adiga, Siddharth Joshi, Spandan Das, Zhengping Wang, Bogdan Gaza, Ari Morcos, Matthew L. Leavitt
2025 J jnl
Luxical: High-Speed Lexical-Dense Text Embeddings.
CoRR
Luke Merrick, Alex Fang, Aldo Carranza, Alvin Deng, Amro Abbas, Brett W. Larsen, Cody Blakeney, Darren Teh, David Schwab, Fan Pan, Haakon Mongstad, Haoli Yin, Jack Urbanek, Jason Lee, Jason Telanoff, Josh Wills, Kaleigh Mentzer, Paul Burstein, Parth Doshi, Paul Burnstein, Pratyush Maini, Ricardo Monti, Rishabh Adiga, Scott Loftin, Siddharth Joshi, Spandan Das, Tony Jiang, Vineeth Dorna, Zhengping Wang, Bogdan Gaza, Ari Morcos, Matthew L. Leavitt
2025 A* conf
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models.
ICLR
Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion, Matthew L. Leavitt, Mansheej Paul
2024 J jnl
Does your data spark joy? Performance gains from domain upsampling at the end of training.
CoRR
Cody Blakeney, Mansheej Paul, Brett W. Larsen, Sean Owen, Jonathan Frankle
2024 J jnl
LoRA Learns Less and Forgets Less.
CoRR
Dan Biderman, Jose Javier Gonzalez Ortiz, Jacob P. Portes, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, John P. Cunningham
2024 J jnl
LoRA Learns Less and Forgets Less.
Trans. Mach. Learn. Res.
Dan Biderman, Jacob P. Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, John Patrick Cunningham
2024 J jnl
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models.
CoRR
Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion, Matthew L. Leavitt, Mansheej Paul
2022 — conf
Measuring Bias and Fairness in Multiclass Classification.
NAS
Cody Blakeney, Gentry Atkinson, Nathaniel Huish, Yan Yan, Vangelis Metsis, Ziliang Zong
2022 J jnl
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation.
CoRR
Cody Blakeney, Jessica Zosa Forde, Jonathan Frankle, Ziliang Zong, Matthew L. Leavitt
2021 J jnl
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks.
CoRR
Cody Blakeney, Gentry Atkinson, Nathaniel Huish, Yan Yan, Vangelis Metsis, Ziliang Zong
2021 J jnl
Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression.
IEEE Trans. Parallel Distributed Syst.
Cody Blakeney, Xiaomin Li, Yan Yan, Ziliang Zong
2021 J jnl
Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation.
CoRR
Cody Blakeney, Nathaniel Huish, Yan Yan, Ziliang Zong
2020 — conf
Craft Distillation: Layer-wise Convolutional Neural Network Distillation.
CSCloud/EdgeCom
Cody Blakeney, Xiaomin Li, Yan Yan, Ziliang Zong
2020 A conf
Is Pruning Compression?: Investigating Pruning Via Network Layer Similarity.
WACV
Cody Blakeney, Yan Yan, Ziliang Zong
2020 J jnl
Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression.
CoRR
Cody Blakeney, Xiaomin Li, Yan Yan, Ziliang Zong
2019 J jnl
Improving the energy efficiency of relational and NoSQL databases via query optimizations.
Sustain. Comput. Informatics Syst.
Divya Mahajan, Cody Blakeney, Ziliang Zong