Le traitement du langage naturel à l'ère de l'IA générative
Rejoignez-nous à Mila en octobre pour un atelier de trois jour visant à explorer le potentiel de transformation des technologies langagières et leurs implications pour la société.
Ce programme est conçu pour fournir aux professionnel·le·s travaillant dans le domaine de la politique une compréhension fondamentale de la technologie de l'IA.
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Publications
How Do the Open Source Communities Address Usability and UX Issues?: An Exploratory Study
Usability and user experience (UX) issues are often not well emphasized and addressed in open source software (OSS) development. There is an… (voir plus) imperative need for supporting OSS communities to collaboratively identify, understand, and fix UX design issues in a distributed environment. In this paper, we provide an initial step towards this effort and report on an exploratory study that investigated how the OSS communities currently reported, discussed, negotiated, and eventually addressed usability and UX issues. We conducted in-depth qualitative analysis of selected issue tracking threads from three OSS projects hosted on GitHub. Our findings indicated that discussions about usability and UX issues in OSS communities were largely influenced by the personal opinions and experiences of the participants. Moreover, the characteristics of the community may have greatly affected the focus of such discussion.
2018-04-20
Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (publié)
Minimizing a function over an intersection of convex sets is an important task in optimization that is often much more challenging than mini… (voir plus)mizing it over each individual constraint set. While traditional methods such as Frank-Wolfe (FW) or proximal gradient descent assume access to a linear or quadratic oracle on the intersection, splitting techniques take advantage of the structure of each sets, and only require access to the oracle on the individual constraints. In this work, we develop and analyze the Frank-Wolfe Augmented Lagrangian (FW-AL) algorithm, a method for minimizing a smooth function over convex compact sets related by a "linear consistency" constraint that only requires access to a linear minimization oracle over the individual constraints. It is based on the Augmented Lagrangian Method (ALM), also known as Method of Multipliers, but unlike most existing splitting methods, it only requires access to linear (instead of quadratic) minimization oracles. We use recent advances in the analysis of Frank-Wolfe and the alternating direction method of multipliers algorithms to prove a sublinear convergence rate for FW-AL over general convex compact sets and a linear convergence rate for polytopes.
Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent succ… (voir plus)esses of nonlinear models in machine learning, it is natural to wonder whether extending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinear WFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFA and relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.
2018-03-31
International Conference on Artificial Intelligence and Statistics (published)
Although deep nets have resulted in high accuracies for various visual tasks, their computational and space requirements are prohibitively h… (voir plus)igh for inclusion on devices without high-end GPUs. In this paper, we introduce a neuron/filter level pruning framework based on Fisher's LDA which leads to high accuracies for a wide array of facial trait classification tasks, while significantly reducing space/computational complexities. The approach is general and can be applied to convolutional, fully-connected, and module-based deep structures, in all cases leveraging the high decorrelation of neuron activations found in the pre-decision layer and cross-layer deconv dependency. Experimental results on binary and multi-category facial traits from the LFWA and Adience datasets illustrate the framework's comparable/better performance to state-of-the-art pruning approaches and compact structures (e.g. SqueezeNet, MobileNet). Ours successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG-16, 82% for GoogLeNet) and with significant FLOP reductions (83% for VGG-16, 64% for GoogLeNet).
With deep learning's success, a limited number of popular deep nets have been widely adopted for various vision tasks. However, this usually… (voir plus) results in unnecessarily high complexities and possibly many features of low task utility. In this paper, we address this problem by introducing a task-dependent deep pruning framework based on Fisher's Linear Discriminant Analysis (LDA). The approach can be applied to convolutional, fully-connected, and module-based deep network structures, in all cases leveraging the high decorrelation of neuron motifs found in the pre-decision layer and cross-layer deconv dependency. Moreover, we examine our approach's potential in network architecture search for specific tasks and analyze the influence of our pruning on model robustness to noises and adversarial attacks. Experimental results on datasets of generic objects, as well as domain specific tasks (CIFAR100, Adience, and LFWA) illustrate our framework's superior performance over state-of-the-art pruning approaches and fixed compact nets (e.g. SqueezeNet, MobileNet). The proposed method successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG16, up to 82% for the already compact InceptionNet) and with significant FLOP reductions (83% for VGG16, up to 64% for InceptionNet). Through pruning, we can also derive smaller, but more accurate and more robust models suitable for the task.
Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals… (voir plus) as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel information theoretical bounds. We introduce a novel training objective for simultaneously training a predictor over target variables of interest (the regular labels) while preventing an intermediate representation to be predictive of the private labels. The architecture is based on three sub-networks: one going from input to representation, one from representation to predicted regular labels, and one from representation to predicted private labels. The training procedure aims at learning representations that preserve the relevant part of the information (about regular labels) while dismissing information about the private labels which correspond to the identity of a person. We demonstrate the success of this approach for two distinct classification versus anonymization tasks (handwritten digits and sentiment analysis).
Abstract. We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented … (voir plus)reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient’s cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.