Publications

Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning
Zhongyu Li
Xue Bin Peng
Pieter Abbeel
Sergey Levine
Glen Berseth
Koushil Sreenath
2022-12-31
arXiv.org (prépublication)
doi.org
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
SORBETmatcher results for OAEI 2023.
Francis Gosselin
Amal Zouaq
2022-12-31
OM@ISWC (publié)
dblp.uni-trier.de
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
Stochastic Generative Flow Networks
Ling Pan
Dinghuai Zhang
Moksh Jain
Longbo Huang
Yoshua Bengio
2022-12-31
Conference on Uncertainty in Artificial Intelligence (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
Supplementary Material for MixupE
Yingtian Zou
Vikas Verma
Sarthak Mittal
Wai Hoh Tang
Hieu Pham
Juho Kannala
Yoshua Bengio
Arno Solin
Kenji Kawaguchi
We denote by z = (x,y) the input and output pair where x ∈ X ⊆ R and y ∈ Y ⊆ R . Let fθ(x) ∈ R be the output of the logits (i.e.,… (voir plus) the last layer before the softmax or sigmoid) of the model parameterized by θ. We use l(θ, z) = h(fθ(x)) − yfθ(x) to denote the loss function. Let g(·) be the activation function. We use x(i) to index i-th element of the vector x and xj to represent j-th variable in a set. The notation list is:
2022-12-31
(publié)
www.semanticscholar.org
A Survey of Diversification Metrics and Approaches in Retrieval Systems: From the Perspective of Search and Recommendation
Haolun Wu
Yansen Zhang
Chen Ma
Fuyuan Lyu
Fernando Diaz
Xue Liu
Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (voir plus)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems
2022-12-31
(publié)
www.semanticscholar.org
Survey of Scientific Rigor Studied in Machine Learning
D. Sculley
Gary R. Holt
Daniel R. Golovin
Eugene V. Davydov
Todd Phillips
Dietmar Ebner
Michael Young
Jean-francois Crespo
Dan Dennison
Emily Fox
Hugo Larochelle
The concern that Artificial Intelligence (AI) and Machine Learning (ML) are entering a “reproducibility crisis” has spurred significant … (voir plus)research in the past few years. Yet with each paper, it is often unclear what someone means by “reproducibility” and where it fits in the larger scope of what we will call the “scientific rigor” literature. Ultimately, the lack of clear rigor standards can affect the manner in which businesses seeking to adopt AI/ML implement such capabilities. In this survey, we will use 66 papers published since 2017 to construct a proposed set of 8 high-level categories of scientific rigor, what they are, and the history of work conducted in each. Our proposal is that these eight rigor types are not mutually exclusive and present a model for how they influence each other. To encourage more to study these questions, we map these rigors to the adoption process in real-world business use cases. In doing so, we can quantify gaps in the literature that suggest an under focus on the issues necessary for scientific rigor research to transition to practice
2022-12-31
(publié)
www.semanticscholar.org