Publications

Iterative Graph Self-Distillation
Hanlin Zhang
Shuai Lin
Weiyang Liu
Pan Zhou
Jian Tang
Xiaodan Liang
Eric P. Xing
Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a met… (voir plus)hod called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a teacher-student distillation process that uses graph diffusion augmentations and constructs the teacher model using an exponential moving average of the student model. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by jointly regularizing the network with both supervised and self-supervised contrastive loss. Finally, we show that fine-tuning the IGSD-trained models with self-training can further improve graph representation learning. Empirically, we achieve significant and consistent performance gain on various graph datasets in both unsupervised and semi-supervised settings, which well validates the superiority of IGSD.
2024-03-01
IEEE Transactions on Knowledge and Data Engineering (publié)
doi.org
openreview.net
Neural network prediction of the effect of thermomechanical controlled processing on mechanical properties
Sushant Sinha
Denzel Guye
Xiaoping Ma
Kashif Rehman
S. Yue
Narges Armanfard
2024-03-01
Machine Learning with Applications (publié)
doi.org
Novel community data in ecology-properties and prospects.
Florian Hartig
Nerea Abrego
Alex Bush
Jonathan M. Chase
G. Guillera‐Arroita
M. Leibold
Otso T. Ovaskainen
Loïc Pellissier
Maximilian Pichler
Giovanni Poggiato
Laura J. Pollock
Sara Si-moussi
Wilfried Thuiller
Duarte S Viana
D. Warton
Damaris Zurell
Douglas W. Yu
2024-03-01
Trends in Ecology & Evolution (publié)
doi.org
arxiv.org
Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.
Vania Karami
Mark J. Yaffe
Genevieve Gore
AJung Moon
Samira Abbasgholizadeh-Rahimi
2024-03-01
Archives of gerontology and geriatrics (Print) (publié)
doi.org
Sources of richness and ineffability for phenomenally conscious states
Xu Ji
Eric Elmoznino
George Deane
Axel Constant
Guillaume Dumas
Guillaume Lajoie
Jonathan Simon
Yoshua Bengio
2024-03-01
Neuroscience of Consciousness (publié)
doi.org
arxiv.org
The « jingle-jangle fallacy » of empathy: Delineating affective, cognitive and motor components of empathy from behavioral synchrony using a virtual agent
Julia Ayache
Guillaume Dumas
Alexander Sumich
D. Kuss
Darren Rhodes
Nadja Heym
2024-03-01
Personality and Individual Differences (publié)
doi.org
COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation
Maxime DARRIN
Philippe Formont
Jackie Chi Kit Cheung
Pablo Piantanida
Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that as… (voir plus)sesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probability of these tasks and the mutual information between source texts and generated summaries. We introduce
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
Crowdkeeping in Last-mile Delivery
Xin Wang
Okan Arslan
Erick Delage
2024-02-29
Transportation Science (publié)
doi.org
Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
Hado van Hasselt
Razvan Pascanu
James Martens
Will Dabney
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov
Raymond Li
Loubna Ben allal
Federico Cassano
Joel Lamy-Poirier
Nouamane Tazi
Ao Tang
Dmytro Pykhtar
Jiawei Liu
Yuxiang Wei
Tianyang Liu
Max Tian
Denis Kocetkov
Arthur Zucker
Younes Belkada
Zijian Wang
Qian Liu
Dmitry Abulkhanov
Indraneil Paul
Zhuang Li … (voir 46 de plus)
Wen-Ding Li
Megan L. Risdal
Jia LI
Jian Zhu
Terry Yue Zhuo
Evgenii Zheltonozhskii
Nii Osae Osae Dade
Wenhao Yu
Lucas Krauss
Naman Jain
Yixuan Su
Xuanli He
Manan Dey
Edoardo Abati
Yekun Chai
Niklas Muennighoff
Xiangru Tang
Muhtasham Oblokulov
Christopher Akiki
Marc Marone
Chenghao Mou
Mayank Mishra
Alex Gu
Binyuan Hui
Tri Dao
Armel Zebaze
Olivier Dehaene
Nicolas Patry
Canwen Xu
Julian McAuley
Han Hu
Torsten Scholak
Sebastien Paquet
Jennifer Robinson
Carolyn Jane Anderson
Nicolas Chapados
Md. Mostofa Ali Patwary
Nima Tajbakhsh
Yacine Jernite
Carlos Muñoz Ferrandis
Lingming Zhang
Sean Hughes
Thomas Wolf
Arjun Guha
Leandro Von Werra
Harm de Vries
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov
Raymond Li
Loubna Ben allal
Federico Cassano
Joel Lamy-Poirier
Nouamane Tazi
Ao Tang
Dmytro Pykhtar
Jiawei Liu
Yuxiang Wei
Tianyang Liu
Max Tian
Denis Kocetkov
Arthur Zucker
Younes Belkada
Zijian Wang
Qian Liu
Dmitry Abulkhanov
Indraneil Paul
Zhuang Li … (voir 46 de plus)
Wen-Ding Li
Megan L. Risdal
Jia LI
Jian Zhu
Terry Yue Zhuo
Evgenii Zheltonozhskii
Nii Osae Osae Dade
Wenhao Yu
Lucas Krauss
Naman Jain
Yixuan Su
Xuanli He
Manan Dey
Edoardo Abati
Yekun Chai
Niklas Muennighoff
Xiangru Tang
Muhtasham Oblokulov
Christopher Akiki
Marc Marone
Chenghao Mou
Mayank Mishra
Alex Gu
Binyuan Hui
Tri Dao
Armel Zebaze
Olivier Dehaene
Nicolas Patry
Canwen Xu
Julian McAuley
Han Hu
Torsten Scholak
Sebastien Paquet
Jennifer Robinson
Carolyn Jane Anderson
Nicolas Chapados
Mostofa Ali Patwary
Nima Tajbakhsh
Yacine Jernite
Carlos Muñoz Ferrandis
Lingming Zhang
Sean Hughes
Thomas Wolf
Arjun Guha
Leandro Von Werra
Harm de Vries
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
The use of dose surface maps as a tool to investigate spatial dose delivery accuracy for the rectum during prostate radiotherapy
Haley Patrick
John Kildea
2024-02-29
Journal of Applied Clinical Medical Physics (publié)
doi.org