Publications

Multi-Agent Estimation and Filtering for Minimizing Team Mean-Squared Error
Mohammad Afshari
Aditya Mahajan
Motivated by estimation problems arising in autonomous vehicles and decentralized control of unmanned aerial vehicles, we consider multi-age… (voir plus)nt estimation and filtering problems in which multiple agents generate state estimates based on decentralized information and the objective is to minimize a coupled mean-squared error which we call team mean-square error. We call the resulting estimates as minimum team mean-squared error (MTMSE) estimates. We show that MTMSE estimates are different from minimum mean-squared error (MMSE) estimates. We derive closed-form expressions for MTMSE estimates, which are linear function of the observations where the corresponding gain depends on the weight matrix that couples the estimation error. We then consider a filtering problem where a linear stochastic process is monitored by multiple agents which can share their observations (with delay) over a communication graph. We derive expressions to recursively compute the MTMSE estimates. To illustrate the effectiveness of the proposed scheme we consider an example of estimating the distances between vehicles in a platoon and show that MTMSE estimates significantly outperform MMSE estimates and consensus Kalman filtering estimates.
2020-12-31
IEEE Transactions on Signal Processing (publié)
doi.org
arxiv.org
2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T
Nilser J. Laines Medina
Charley Gros
Julien Cohen-Adad
Virginie Callot
Arnaud Le Troter
The spinal cord (SC), which conveys information between the brain and the peripheral nervous system, plays a key role in various neurologica… (voir plus)l disorders such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in which both gray matter (GM) and white matter (WM) may be impaired. While automated methods for WM/GM segmentation are now largely available, these techniques, developed for conventional systems (3T or lower) do not necessarily perform well on 7T MRI data, which feature finer details, contrasts, but also different artifacts or signal dropout. The primary goal of this study is thus to propose a new deep learning model that allows robust SC/GM multi-class segmentation based on ultra-high resolution 7T T2*-w MR images. The second objective is to highlight the relevance of implementing a specific data augmentation (DA) strategy, in particular to generate a generic model that could be used for multi-center studies at 7T.
2020-12-31
arXiv (prépublication)
doi.org
arxiv.org
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding
Dmitriy Serdyuk
Yongqiang Wang
Christian Fue-730
Anuj Kumar
Baiyang Liu
Yoshua Bengio
Edwin Simonnet
Sahar Ghannay
Nathalie Camelin
Tandem spoken language understanding 001 (SLU) systems suffer from the so-called 002 automatic speech recognition (ASR) error 003 propagatio… (voir plus)n problem. Additionally, as the 004 ASR is not optimized to extract semantics, but 005 solely the linguistic content, relevant semantic 006 cues might be left out of its transcripts. In 007 this work, we propose a multimodal language 008 understanding (MLU) architecture to mitigate 009 these problems. Our solution is based on 010 two compact unidirectional long short-term 011 memory (LSTM) models that encode speech 012 and text information. A fusion layer is also 013 used to fuse audio and text embeddings. 014 Two fusion strategies are explored: a simple 015 concatenation of these embeddings and a 016 cross-modal attention mechanism that learns 017 the contribution of each modality. The first 018 approach showed to be the optimal solution 019 to robustly extract semantic information from 020 audio-textual data. We found that attention 021 is less effective at testing time when the text 022 modality is corrupted. Our model is evaluated 023 on three SLU datasets and robustness is tested 024 using ASR outputs from three off-the-shelf 025 ASR engines. Results show that the proposed 026 approach effectively mitigates the ASR error 027 propagation problem for all datasets. 028
2020-12-31
(publié)
www.semanticscholar.org
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data
Michal Gerasimiuk
Dennis Shung
Alexander Tong
Adrian Stanley
Michael Schultz
Jeffrey Ngu
Loren Laine
Guy Wolf
Smita Krishnaswamy
A major challenge in embedding or visualizing clinical patient data is the heterogeneity of variable types including continuous lab values, … (voir plus)categorical diagnostic codes, as well as missing or incomplete data. In particular, in EHR data, some variables are {\em missing not at random (MNAR)} but deliberately not collected and thus are a source of information. For example, lab tests may be deemed necessary for some patients on the basis of suspected diagnosis, but not for others. Here we present the MURAL forest -- an unsupervised random forest for representing data with disparate variable types (e.g., categorical, continuous, MNAR). MURAL forests consist of a set of decision trees where node-splitting variables are chosen at random, such that the marginal entropy of all other variables is minimized by the split. This allows us to also split on MNAR variables and discrete variables in a way that is consistent with the continuous variables. The end goal is to learn the MURAL embedding of patients using average tree distances between those patients. These distances can be fed to nonlinear dimensionality reduction method like PHATE to derive visualizable embeddings. While such methods are ubiquitous in continuous-valued datasets (like single cell RNA-sequencing) they have not been used extensively in mixed variable data. We showcase the use of our method on one artificial and two clinical datasets. We show that using our approach, we can visualize and classify data more accurately than competing approaches. Finally, we show that MURAL can also be used to compare cohorts of patients via the recently proposed tree-sliced Wasserstein distances.
2020-12-31
IEEE International Conference on Big Data (inconnu)
doi.org
arxiv.org
On-the-Fly Attention Modularization for Neural Generation
Yue Dong
Chandra Bhagavatula
Ximing Lu
Jena D. Hwang
Antoine Bosselut
Jackie CK Cheung
Yejin Choi
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from de generation: generated… (voir plus) text is repetitive, generic, self-inconsistent, and lacking commonsense. The empirical analyses on sentence-level attention patterns reveal that neural text degeneration may be associated with insufficient learning of inductive biases by the attention mechanism. Our findings motivate on-the-fly attention modularization, a simple but effective method for injecting inductive biases into attention computation during inference. The resulting text produced by the language model with attention modularization can yield enhanced diversity and commonsense reasoning while maintaining fluency and coherence.
2020-12-31
arXiv.org (prépublication)
dblp.uni-trier.de
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata
Borja Balle
Clara Lacroce
Prakash Panangaden
Doina Precup
Guillaume Rabusseau
We address the approximate minimization problem for weighted finite automata (WFAs) with weights in …
2020-12-31
Leibniz-Zentrum für Informatik (publié)
doi.org
arxiv.org
Optimization of Artificial Neural Network Hyperparameters For Processing Retrospective Information
A. Rogachev
F. Scholle
Yann Lecun
Yoshua Bengio
I. L. Kashirin
M. Demchenko
. Justification of the selection of the architecture and hyperparameters of artificial neural networks (ANN), focused on solving various cla… (voir plus)sses of applied problems, is a scientific and methodological problem. Optimizing the selection of ANN hyperparameters allows you to improve the quality and speed of ANN training. Various methods of optimizing the selection of ANN hyper-parameters are known – the use of evolutionary calculations, genetic algorithms, etc., but they require the use of additional software. To optimize the process of selecting ANN hyperparameters, Google Research has developed the KerasTuner software tool. It is a platform for automated search of a set of optimal combinations of hyperparameters. In Kerastuner, you can use various methods - random search, Bayesian optimization, or Hyperband. In the numerical experiments conducted by the author, 14 hyperparameters were varied, including the number of blocks of convolutional layers and the filters forming them, the type of activation function, the parameters of the "dropout" layers, and others. The studied tools demonstrated high efficiency while simultaneously varying more than a dozen optimized parameters of the convolutional network. The calculation time on the Colaboratory platform for the various combined ANN architectures studied, including recurrent RNN networks, was several hours, even with the use of GPU graphics accelerators. For ANN, focused on the processing and recognition of retrospective information, an increase in the quality of recognition was achieved to 80 ... 95%.
2020-12-31
(publié)
www.semanticscholar.org
Overview of the TREC 2021 Fair Ranking Track
Asia J. Biega
Fernando Diaz
Michael D. Ekstrand
Sebastian Kohlmeier
The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide … (voir plus)a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents' topical content or authors. The 2021 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2021 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.
2020-12-31
TREC (publié)
doi.org
arxiv.org
Personalized Medicine for OSA Syndrome in a Nutshell: Conceptual Clarification for Integration.
Christophe Gauld
Marie Darrason
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2020-12-31
Chest (publié)
doi.org
RAFFIC V IS : Fighting Human Trafficking through Visualization
Catalina Vajiac
Andreas Olligschlaeger
Yifei Li
Pratheeksha Nair
Meng-Chieh Lee
Namyong Park
Reihaneh Rabbany
Duen Horng Chau
Christos Faloutsos
Law enforcement can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected ads. Given such cl… (voir plus)usters, how can we interactively visualize potential evidence for law enforcement and domain experts? We present TRAFFICVIS, which, to our knowledge, is the first interface for cluster-level HT detection and labeling. It builds on state-of-the-art HT clustering algorithms by incorporating metadata as a signal of organized and potentially suspicious activity. Also, domain experts can label clusters as HT, spam, and more, efficiently creating labeled datasets to enable further HT research. TRAFFICVIS has been built in close collaboration with domain experts, who estimate that TRAFFICVIS provides a median 36x speedup over manual labeling.
2020-12-31
(publié)
www.semanticscholar.org
Randomized Least Squares Policy Optimization
Haque Ishfaq
Zhuoran Yang
Andrei Lupu
Viet Bang Nguyen
Lewis Liu
Riashat Islam
Zhaoran Wang
Doina Precup
Policy Optimization (PO) methods with function approximation are one of the most popular classes of Reinforcement Learning (RL) algorithms. … (voir plus)However, designing provably efficient policy optimization algorithms remains a challenge. Recent work in this area has focused on incorporating upper confidence bound (UCB)-style bonuses to drive exploration in policy optimization. In this paper, we present Randomized Least Squares Policy Optimization (RLSPO) which is inspired by Thompson Sampling. We prove that, in an episodic linear kernel MDP setting, RLSPO achieves (cid:101) O ( d 3 / 2 H 3 / 2 √ T ) worst-case (frequentist) regret, where H is the number of episodes, T is the total number of steps and d is the feature dimension. Finally, we evaluate RLSPO empirically and show that it is competitive with existing provably efficient PO algorithms.
2020-12-31
(publié)
www.semanticscholar.org
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems
Mukul Gagrani
Sagar Sudhakara
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
—We revisit the Thompson sampling algorithm to control an unknown linear quadratic (LQ) system recently proposed by Ouyang et al. [1]. The… (voir plus) regret bound of the algorithm was derived under a technical assumption on the induced norm of the closed loop system. In this technical note, we show that by making a minor modification in the algorithm (in particular, ensuring that an episode does not end too soon), this technical assumption on the induced norm can be replaced by a milder assumption in terms of the spectral radius of the closed loop system. The modified algorithm has the same Bayesian regret of ˜ O ( √ T ) , where T is the time-horizon and the ˜ O ( · ) notation hides logarithmic terms in T .
2020-12-31
arXiv.org (prépublication)
dblp.uni-trier.de