Publications

qu an tph ] 10 O ct 2 01 1 Quantum Communication in Rindler Spacetime
Kamil Brádler
P. Hayden
Prakash Panangaden
A state that an inertial observer in Minkowski space perceiv es to be the vacuum will appear to an accelerating observer to be a thermal ba … (voir plus)th of radiation. We study the impact of this Davies-Fulling-Unruh noise on comm unication, particularly quantum communication from an inertial sender to an ac celerating observer and private communication between two inertial observers i n the presence of an accelerating eavesdropper. In both cases, we establish com pact, tractable formulas for the associated communication capacities assuming enco dings that allow a single excitation in one of a fixed number of modes per use of the co mmunications channel. Our contributions include a rigorous presentatio n of the general theory of the private quantum capacity as well as a detailed analysis o f the structure of these channels, including their group-theoretic properties and proof that they are conjugate degradable. Connections between the Unruh channel a d optical amplifiers are also discussed.
2020-12-31
(publié)
www.semanticscholar.org
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
Vincent Dumoulin
Neil Houlsby
Utku Evci
Xiaohua Zhai
Ross Goroshin
Sylvain Gelly
Hugo Larochelle
Meta and transfer learning are two successful families of approaches to few-shot 1 learning. Despite highly related goals, state-of-the-art … (voir plus)advances in each family are 2 measured largely in isolation of each other. As a result of diverging evaluation 3 norms, a direct or thorough comparison of different approaches is challenging. 4 To bridge this gap, we introduce a few-shot classification evaluation protocol 5 named VTAB+MD with the explicit goal of facilitating sharing of insights from 6 each community. We demonstrate its accessibility in practice by performing a 7 cross-family study of the best transfer and meta learners which report on both a 8 large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning 9 benchmark (Visual Task Adaptation Benchmark, VTAB). We find that, on average, 10 large-scale transfer methods (Big Transfer, BiT) outperform competing approaches 11 on MD, even when trained only on ImageNet. In contrast, meta-learning approaches 12 struggle to compete on VTAB when trained and validated on MD. However, BiT 13 is not without limitations, and pushing for scale does not improve performance 14 on highly out-of-distribution MD tasks. We hope that this work contributes to 15 accelerating progress on few-shot learning research. 16
2020-12-31
NeurIPS Datasets and Benchmarks (publié)
openreview.net
openreview.net
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond
Dinghuai Zhang
Jie Fu
Yoshua Bengio
Aaron Courville
2020-12-31
arXiv.org (prépublication)
dblp.uni-trier.de
A Universal Representation Transformer Layer for Few-Shot Image Classification
Li Li
William L. Hamilton
Guodong Long
Jing Jiang
Hugo Larochelle
Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of mult… (voir plus)i-domain few-shot image classification, where unseen classes and examples come from diverse data sources. This problem has seen growing interest and has inspired the development of benchmarks such as Meta-Dataset. A key challenge in this multi-domain setting is to effectively integrate the feature representations from the diverse set of training domains. Here, we propose a Universal Representation Transformer (URT) layer, that meta-learns to leverage universal features for few-shot classification by dynamically re-weighting and composing the most appropriate domain-specific representations. In experiments, we show that URT sets a new state-of-the-art result on Meta-Dataset. Specifically, it achieves top-performance on the highest number of data sources compared to competing methods. We analyze variants of URT and present a visualization of the attention score heatmaps that sheds light on how the model performs cross-domain generalization.
2020-12-31
ICLR (publié)
openreview.net
openreview.net
Using Artificial Intelligence to Visualize the Impacts of Climate Change
Alexandra Luccioni
Victor Schmidt
Vahe Vardanyan
Yoshua Bengio
T. Rhyne
Public awareness and concern about climate change often do not match the magnitude of its threat to humans and our environment. One reason f… (voir plus)or this disagreement is that it is difficult to mentally simulate the effects of a process as complex as climate change and to have a concrete representation of the impact that our individual actions will have on our own future, especially if the consequences are long term and abstract. To overcome these challenges, we propose to use cutting-edge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, the AI climate impact visualizer. It will allow a user to enter an address—be it their house, their school, or their workplace—-and it will provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change such as floods, storms, and wildfires. This image will be accompanied by accessible information regarding the science behind climate change, i.e., why extreme weather events are becoming more frequent and what kinds of changes are happening on a local and global scale.
2020-12-31
IEEE Computer Graphics and Applications (publié)
doi.org
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Chin-wei Huang
Jae Hyun Lim
Aaron Courville
Discrete-time diffusion-based generative models and score matching methods have shown promising results in modeling high-dimensional image d… (voir plus)ata. Recently, Song et al. (2021) show that diffusion processes that transform data into noise can be reversed via learning the score function, i.e. the gradient of the log-density of the perturbed data. They propose to plug the learned score function into an inverse formula to define a generative diffusion process. Despite the empirical success, a theoretical underpinning of this procedure is still lacking. In this work, we approach the (continuous-time) generative diffusion directly and derive a variational framework for likelihood estimation, which includes continuous-time normalizing flows as a special case, and can be seen as an infinitely deep variational autoencoder. Under this framework, we show that minimizing the score-matching loss is equivalent to maximizing a lower bound of the likelihood of the plug-in reverse SDE proposed by Song et al. (2021), bridging the theoretical gap.
2020-12-31
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (publié)
openreview.net
openreview.net
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types
Susan Bartlett
Grzegorz Kondrak
Max Bartolo
Alastair Roberts
Johannes Welbl
Steven Bird
Ewan Klein
Edward Loper
Samuel R. Bowman
George Dahl. 2021
What
Chao Pang
Junyuan Shang
Jiaxiang Liu
Xuyi Chen
Yanbin Zhao
Yuxiang Lu
Weixin Liu
Zhi-901 hua Wu
Weibao Gong … (voir 21 de plus)
Jianzhong Liang
Zhizhou Shang
Peng Sun
Ouyang Xuan
Dianhai
Houwen Tian
Hua Wu
Haifeng Wang
Adam Trischler
Tong Wang
Xingdi Yuan
Justin Har-908
Alessandro Sordoni
Philip Bachman
Adina Williams
Nikita Nangia
Zhilin Yang
Peng Qi
Saizheng Zhang
Yoshua Bengio
ing. In
For a natural language understanding bench-001 mark to be useful in research, it has to con-002 sist of examples that are diverse and diffi… (voir plus)-003 cult enough to discriminate among current and 004 near-future state-of-the-art systems. However, 005 we do not yet know how best to select pas-006 sages to collect a variety of challenging exam-007 ples. In this study, we crowdsource multiple-008 choice reading comprehension questions for 009 passages taken from seven qualitatively dis-010 tinct sources, analyzing what attributes of pas-011 sages contribute to the difficulty and question 012 types of the collected examples. To our sur-013 prise, we find that passage source, length, and 014 readability measures do not significantly affect 015 question difficulty. Through our manual anno-016 tation of seven reasoning types, we observe 017 several trends between passage sources and 018 reasoning types, e.g., logical reasoning is more 019 often required in questions written for techni-020 cal passages. These results suggest that when 021 creating a new benchmark dataset, selecting a 022 diverse set of passages can help ensure a di-023 verse range of question types, but that passage 024 difficulty need not be a priority. 025
2020-12-31
(publié)
www.semanticscholar.org
Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
Maziar Gomrokchi
Susan Amin
Hossein Aboutalebi
Alexander Wong
Doina Precup
to
2020-12-31
arXiv.org (prépublication)
dblp.uni-trier.de
Your head is there to move you around: Goal-driven models of the primate dorsal pathway
Patrick J Mineault
Shahab Bakhtiari
Blake A Richards
Christopher C Pack
Neurons in the dorsal visual pathway of the mammalian brain are selective for motion stimuli, with the complexity of stimulus representation… (voir plus)s increasing along the hierarchy. This progression is similar to that of the ventral visual pathway, which is well characterized by artificial neural networks (ANNs) optimized for object recognition. In contrast, there are no image-computable models of the dorsal stream with comparable explanatory power. We hypothesized that the properties of dorsal stream neurons could be explained by a simple learning objective: the need for an organism to orient itself during self-motion. To test this hypothesis, we trained a 3D ResNet to predict an agent’s self-motion parameters from visual stimuli in a simulated environment. We found that the responses in this network accounted well for the selectivity of neurons in a large database of single-neuron recordings from the dorsal visual stream of non-human primates. In contrast, ANNs trained on an action recognition dataset through supervised or self-supervised learning could not explain responses in the dorsal stream, despite also being trained on naturalistic videos with moving objects. These results demonstrate that an ecologically relevant cost function can account for dorsal stream properties in the primate brain.
2020-12-31
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (publié)
doi.org
openreview.net
Mass‐spectrometry analysis of the human pineal proteome during night and day and in autism
Guillaume Dumas
Hany Goubran‐Botros
Mariette Matondo
Cécile Pagan
Cyril Boulègue
Thibault Chaze
Julia Chamot‐Rooke
Erik Maronde
Thomas Bourgeron
The human pineal gland regulates day‐night dynamics of multiple physiological processes, especially through the secretion of melatonin. Us… (voir plus)ing mass‐spectrometry‐based proteomics and dedicated analysis tools, we identify proteins in the human pineal gland and analyze systematically their variation throughout the day and compare these changes in the pineal proteome between control specimens and donors diagnosed with autism. Results reveal diverse regulated clusters of proteins with, among others, catabolic carbohydrate process and cytoplasmic membrane‐bounded vesicle‐related proteins differing between day and night and/or control versus autism pineal glands. These data show novel and unexpected processes happening in the human pineal gland during the day/night rhythm as well as specific differences between autism donor pineal glands and those from controls.
2020-12-22
Journal of Pineal Research (publié)
doi.org
Bijective-Contrastive Estimation
Jae Hyun Lim
Chin-wei Huang
Aaron Courville
Christopher Pal
In this work, we propose Bijective-Contrastive Estimation (BCE), a classification-based learning criterion for energy-based models. We gener… (voir plus)ate a collection of contrasting distributions using bijections, and solve all the classification problems between the original data distribution and the distributions induced by the bijections using a classifier parameterized by an energy model. We show that if the classification objective is minimized, the energy function will uniquely recover the data density up to a normalizing constant. This has the benefit of not having to explicitly specify a contrasting distribution, like noise contrastive estimation. Experimentally, we demonstrate that the proposed method works well on 2D synthetic datasets. We discuss the difficulty in high dimensional cases, and propose potential directions to explore for future work.
2020-12-20
approximateinference.org/AABI/2021/Symposium (accepté)
openreview.net
openreview.net
Functional idiosyncrasy has a shared topography with group-level connectivity alterations in autism
Oualid Benkarim
Casey Paquola
Bo-yong Park
Seok-Jun Hong
Jessica Royer
Reinder Vos de Wael
Sara Larivière
Sofie Valk
Danilo Bzdok
Laurent Mottron
Boris Bernhardt
Autism spectrum disorder (ASD) is commonly understood as a network disorder, yet case-control analyses against typically-developing controls… (voir plus) (TD) have yielded somewhat inconsistent patterns of results. The current work was centered on a novel approach to profile functional network idiosyncrasy, the inter-individual variability in the association between functional network organization and brain anatomy, and we tested the hypothesis that idiosyncrasy contributes to connectivity alterations in ASD. Studying functional network idiosyncrasy in a multi-centric dataset with 157 ASD and 172 TD, our approach revealed higher idiosyncrasy in ASD in the default mode, somatomotor and attention networks together with reduced idiosyncrasy in the lateral temporal lobe. Idiosyncrasy was found to increase with age in both ASD and TD, and was significantly correlated with symptom severity in the former group. Association analysis with structural and molecular brain features indicated that patterns of functional network idiosyncrasy were not correlated with ASD-related cortical thickness alterations, but closely with the spatial expression patterns of intracortical ASD risk genes. In line with our main hypothesis, we could demonstrate that idiosyncrasy indeed plays a strong role in the manifestation of connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between the heterogeneity of individuals with an autism diagnosis and group-level functional signatures, and help to consolidate prior research findings on the highly variable nature of the functional connectome in ASD. Our study promotes idiosyncrasy as a potential individualized diagnostic marker of atypical brain network development.
2020-12-17
bioRxiv (prépublication)
doi.org