Publications

Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data
Baptiste Bauvin
Cécile Capponi
Florence Clerc
Pascal Germain
Sokol Koço
J. Corbeil
2022-12-31
UAI (publié)
proceedings.mlr.press
proceedings.mlr.press
Scalar Invariant Networks with Zero Bias
Chuqin Geng
Xiaojie Xu
Haolin Ye
Xujie Si
Just like weights, bias terms are the learnable parameters of many popular machine learning models, including neural networks. Biases are th… (voir plus)ought to enhance the representational power of neural networks, enabling them to solve a variety of tasks in computer vision. However, we argue that biases can be disregarded for some image-related tasks such as image classification, by considering the intrinsic distribution of images in the input space and desired model properties from first principles. Our findings suggest that zero-bias neural networks can perform comparably to biased networks for practical image classification tasks. We demonstrate that zero-bias neural networks possess a valuable property called scalar (multiplication) invariance. This means that the prediction of the network remains unchanged when the contrast of the input image is altered. We extend scalar invariance to more general cases, enabling formal verification of certain convex regions of the input space. Additionally, we prove that zero-bias neural networks are fair in predicting the zero image. Unlike state-of-the-art models that may exhibit bias toward certain labels, zero-bias networks have uniform belief in all labels. We believe dropping bias terms can be considered as a geometric prior in designing neural network architecture for image classification, which shares the spirit of adapting convolutions as the transnational invariance prior. The robustness and fairness advantages of zero-bias neural networks may also indicate a promising path towards trustworthy and ethical AI.
2022-12-31
NeurReps (publié)
doi.org
proceedings.mlr.press
Scaling Self-Supervised End-to-End Driving with Multi-View Attention Learning
Yi Xiao
Felipe Codevilla
Diego Porres
Antonio M. López
2022-12-31
arXiv.org (prépublication)
doi.org
Screening methods for congenital anomalies in low and lower-middle income countries: A systematic review.
Justina O. Seyi-Olajide
Xiya Ma
Elena Guadagno
Adesoji Ademuyiwa
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (publié)
doi.org
Self-Influence Guided Data Reweighting for Language Model Pre-training
Megh Thakkar
Tolga Bolukbasi
Sriram Ganapathy
Shikhar Vashishth
Sarath Chandar
Partha Talukdar
Language Models (LMs) pre-trained with self-supervision on large text corpora have become the default starting point for developing models f… (voir plus)or various NLP tasks. Once the pre-training corpus has been assembled, all data samples in the corpus are treated with equal importance during LM pre-training. However, due to varying levels of relevance and quality of data, equal importance to all the data samples may not be the optimal choice. While data reweighting has been explored in the context of task-specific supervised learning and LM fine-tuning, model-driven reweighting for pre-training data has not been explored. We fill this important gap and propose PRESENCE, a method for jointly reweighting samples by leveraging self-influence (SI) scores as an indicator of sample importance and pre-training. PRESENCE promotes novelty and stability for model pre-training. Through extensive analysis spanning multiple model sizes, datasets, and tasks, we present PRESENCE as an important first step in the research direction of sample reweighting for pre-training language models.
2022-12-31
EMNLP (publié)
doi.org
openreview.net
SORBETmatcher results for OAEI 2023.
Francis Gosselin
Amal Zouaq
2022-12-31
OM@ISWC (publié)
dblp.uni-trier.de
Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads
Salah Zaiem
Youcef Kemiche
Titouan Parcollet
Slim Essid
Mirco Ravanelli
Self-supervised learning (SSL) leverages large datasets of unlabeled speech to reach impressive performance with reduced amounts of annotate… (voir plus)d data. The high number of proposed approaches fostered the emergence of comprehensive benchmarks that evaluate their performance on a set of downstream tasks exploring various aspects of the speech signal. However, while the number of considered tasks has been growing, most proposals rely upon a single downstream architecture that maps the frozen SSL representations to the task labels. This study examines how benchmarking results are affected by changes in the probing head architecture. Interestingly, we found that altering the downstream architecture structure leads to significant fluctuations in the performance ranking of the evaluated models. Against common practices in speech SSL benchmarking, we evaluate larger-capacity probing heads, showing their impact on performance, inference costs, generalization and multi-level feature exploitation.
2022-12-31
arXiv (prépublication)
doi.org
arxiv.org
Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets
Jwen Fai Low
Benjamin C. M. Fung
Farkhund Iqbal
COVID-19 is an opportunity to study public acceptance of a “new” healthcare intervention, universal masking, which unlike vaccination, i… (voir plus)s mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, and controversiality and disagreeableness, represented by ratios of the aforementioned counts, favored pro-mask tweets initially but with anti-mask tweets slowly gaining ground. Additional analysis raised the possibility of the platform owners suppressing certain parts of the mask-wearing discussion.
2022-12-31
IEEE Access (publié)
doi.org
StarCoder: may the source be with you!
Raymond Li
Loubna Ben allal
Yangtian Zi
Niklas Muennighoff
Denis Kocetkov
Chenghao Mou
Marc Marone
Christopher Akiki
Jia LI
Jenny Chim
Qian Liu
Evgenii Zheltonozhskii
Terry Yue Zhuo
Thomas Wang
Olivier Dehaene
Mishig Davaadorj
Joel Lamy-Poirier
Joao Monteiro
Oleh Shliazhko
Nicolas Gontier … (voir 47 de plus)
Nicholas Meade
Armel Zebaze
Ming-Ho Yee
Logesh Kumar Umapathi
Jian Zhu
Ben Lipkin
Muhtasham Oblokulov
Zhiruo Wang
Rudra Murthy
Jason T Stillerman
Siva Sankalp Patel
Dmitry Abulkhanov
Marco Zocca
Manan Dey
Zhihan Zhang
N. Fahmy
Urvashi Bhattacharyya
Wenhao Yu
Swayam Singh
Sasha Luccioni
Paulo Villegas
M. Kunakov
Fedor Zhdanov
Manuel Romero
Tony Lee
Nadav Timor
Jennifer Ding
Claire S Schlesinger
Hailey Schoelkopf
Jan Ebert
Tri Dao
Mayank Mishra
Alex Gu
Jennifer Robinson
Carolyn Jane Anderson
Brendan Dolan-Gavitt
Danish Contractor
Siva Reddy
Daniel Fried
Dzmitry Bahdanau
Yacine Jernite
Carlos Muñoz Ferrandis
Sean Hughes
Thomas Wolf
Arjun Guha
Leandro Von Werra
Harm de Vries
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs)… (voir plus), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.
2022-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Stochastic Generative Flow Networks
Ling Pan
Dinghuai Zhang
Moksh J. Jain
Longbo Huang
Yoshua Bengio
2022-12-31
UAI (publié)
doi.org
proceedings.mlr.press
Structure-aware Protein Self-supervised Learning
Can (Sam) Chen
Jingbo Zhou
Fan Wang
Xue Liu
Dejing Dou
Protein representation learning methods have shown great potential to yield useful representation for many downstream tasks, especially on p… (voir plus)rotein classification. Moreover, a few recent studies have shown great promise in addressing insufficient labels of proteins with self-supervised learning methods. However, existing protein language models are usually pretrained on protein sequences without considering the important protein structural information. To this end, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme. Experiments on several supervised downstream tasks verify the effectiveness of our proposed method.The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.
2022-12-31
Bioinformatics (publié)
doi.org
arxiv.org
SUMMIT: Scaffolding OSS Issue Discussion Through Summarization
Saskia Gilmer
Avinash Bhat
Shuvam Shah
Kevin Cherry
Jinghui Cheng
Jin L.C. Guo
2022-12-31
arXiv.org (prépublication)
doi.org