Paper Group ANR 637
Doubly Robust Data-Driven Distributionally Robust Optimization. Efficient Regret Minimization in Non-Convex Games. Listening to the World Improves Speech Command Recognition. BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning. A semidiscrete version of the Citti-Petitot-Sarti model as a plausible model for anthropomorphic imag …
Doubly Robust Data-Driven Distributionally Robust Optimization
| Title | Doubly Robust Data-Driven Distributionally Robust Optimization |
| Authors | Jose Blanchet, Yang Kang, Fan Zhang, Fei He, Zhangyi Hu |
| Abstract | Data-driven Distributionally Robust Optimization (DD-DRO) via optimal transport has been shown to encompass a wide range of popular machine learning algorithms. The distributional uncertainty size is often shown to correspond to the regularization parameter. The type of regularization (e.g. the norm used to regularize) corresponds to the shape of the distributional uncertainty. We propose a data-driven robust optimization methodology to inform the transportation cost underlying the definition of the distributional uncertainty. We show empirically that this additional layer of robustification, which produces a method we called doubly robust data-driven distributionally robust optimization (DD-R-DRO), allows to enhance the generalization properties of regularized estimators while reducing testing error relative to state-of-the-art classifiers in a wide range of data sets. |
| Tasks | |
| Published | 2017-05-19 |
| URL | http://arxiv.org/abs/1705.07168v1 |
| http://arxiv.org/pdf/1705.07168v1.pdf | |
| PWC | https://paperswithcode.com/paper/doubly-robust-data-driven-distributionally |
| Repo | |
| Framework | |
Efficient Regret Minimization in Non-Convex Games
| Title | Efficient Regret Minimization in Non-Convex Games |
| Authors | Elad Hazan, Karan Singh, Cyril Zhang |
| Abstract | We consider regret minimization in repeated games with non-convex loss functions. Minimizing the standard notion of regret is computationally intractable. Thus, we define a natural notion of regret which permits efficient optimization and generalizes offline guarantees for convergence to an approximate local optimum. We give gradient-based methods that achieve optimal regret, which in turn guarantee convergence to equilibrium in this framework. |
| Tasks | |
| Published | 2017-07-31 |
| URL | http://arxiv.org/abs/1708.00075v1 |
| http://arxiv.org/pdf/1708.00075v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-regret-minimization-in-non-convex |
| Repo | |
| Framework | |
Listening to the World Improves Speech Command Recognition
| Title | Listening to the World Improves Speech Command Recognition |
| Authors | Brian McMahan, Delip Rao |
| Abstract | We study transfer learning in convolutional network architectures applied to the task of recognizing audio, such as environmental sound events and speech commands. Our key finding is that not only is it possible to transfer representations from an unrelated task like environmental sound classification to a voice-focused task like speech command recognition, but also that doing so improves accuracies significantly. We also investigate the effect of increased model capacity for transfer learning audio, by first validating known results from the field of Computer Vision of achieving better accuracies with increasingly deeper networks on two audio datasets: UrbanSound8k and the newly released Google Speech Commands dataset. Then we propose a simple multiscale input representation using dilated convolutions and show that it is able to aggregate larger contexts and increase classification performance. Further, the models trained using a combination of transfer learning and multiscale input representations need only 40% of the training data to achieve similar accuracies as a freshly trained model with 100% of the training data. Finally, we demonstrate a positive interaction effect for the multiscale input and transfer learning, making a case for the joint application of the two techniques. |
| Tasks | Environmental Sound Classification, Transfer Learning |
| Published | 2017-10-23 |
| URL | http://arxiv.org/abs/1710.08377v1 |
| http://arxiv.org/pdf/1710.08377v1.pdf | |
| PWC | https://paperswithcode.com/paper/listening-to-the-world-improves-speech |
| Repo | |
| Framework | |
BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning
| Title | BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning |
| Authors | Olivia Michael, Oliver Obst |
| Abstract | RoboCup offers a set of benchmark problems for Artificial Intelligence in form of official world championships since 1997. The most tactical advanced and richest in terms of behavioural complexity of these is the 2D Soccer Simulation League, a simulated robotic soccer competition. BetaRun is a new attempt combining both machine learning and manual programming approaches, with the ultimate goal to arrive at a team that is trained entirely from observing and playing games, and a new development based on agent2D. |
| Tasks | |
| Published | 2017-03-12 |
| URL | http://arxiv.org/abs/1703.04115v2 |
| http://arxiv.org/pdf/1703.04115v2.pdf | |
| PWC | https://paperswithcode.com/paper/betarun-soccer-simulation-league-team-variety |
| Repo | |
| Framework | |
A semidiscrete version of the Citti-Petitot-Sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition
| Title | A semidiscrete version of the Citti-Petitot-Sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition |
| Authors | Dario Prandi, Jean-Paul Gauthier |
| Abstract | In his beautiful book [66], Jean Petitot proposes a sub-Riemannian model for the primary visual cortex of mammals. This model is neurophysiologically justified. Further developments of this theory lead to efficient algorithms for image reconstruction, based upon the consideration of an associated hypoelliptic diffusion. The sub-Riemannian model of Petitot and Citti-Sarti (or certain of its improvements) is a left-invariant structure over the group $SE(2)$ of rototranslations of the plane. Here, we propose a semi-discrete version of this theory, leading to a left-invariant structure over the group $SE(2,N)$, restricting to a finite number of rotations. This apparently very simple group is in fact quite atypical: it is maximally almost periodic, which leads to much simpler harmonic analysis compared to $SE(2).$ Based upon this semi-discrete model, we improve on previous image-reconstruction algorithms and we develop a pattern-recognition theory that leads also to very efficient algorithms in practice. |
| Tasks | Image Reconstruction |
| Published | 2017-04-10 |
| URL | http://arxiv.org/abs/1704.03069v3 |
| http://arxiv.org/pdf/1704.03069v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-semidiscrete-version-of-the-citti-petitot |
| Repo | |
| Framework | |
Bayesian Semi-nonnegative Tri-matrix Factorization to Identify Pathways Associated with Cancer Types
| Title | Bayesian Semi-nonnegative Tri-matrix Factorization to Identify Pathways Associated with Cancer Types |
| Authors | Sunho Park, Tae Hyun Hwang |
| Abstract | Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of various types of cancers better. Tri-matrix factorization is an efficient tool to learn associations between two different entities (e.g., cancer types and pathways in our case) from data. To successfully apply tri-matrix factorization methods to biomedical problems, biological prior knowledge such as pathway databases or protein-protein interaction (PPI) networks, should be taken into account in the factorization model. However, it is not straightforward in the Bayesian setting even though Bayesian methods are more appealing than point estimate methods, such as a maximum likelihood or a maximum posterior method, in the sense that they calculate distributions over variables and are robust against overfitting. We propose a Bayesian (semi-)nonnegative matrix factorization model for human cancer genomic data, where the biological prior knowledge represented by a pathway database and a PPI network is taken into account in the factorization model through a finite dependent Beta-Bernoulli prior. We tested our method on The Cancer Genome Atlas (TCGA) dataset and found that the pathways identified by our method can be used as a prognostic biomarkers for patient subgroup identification. |
| Tasks | |
| Published | 2017-12-01 |
| URL | http://arxiv.org/abs/1712.00520v1 |
| http://arxiv.org/pdf/1712.00520v1.pdf | |
| PWC | https://paperswithcode.com/paper/bayesian-semi-nonnegative-tri-matrix |
| Repo | |
| Framework | |
Approximation learning methods of Harmonic Mappings in relation to Hardy Spaces
| Title | Approximation learning methods of Harmonic Mappings in relation to Hardy Spaces |
| Authors | Zhulin Liu, C. L. Philip Chen |
| Abstract | A new Hardy space Hardy space approach of Dirichlet type problem based on Tikhonov regularization and Reproducing Hilbert kernel space is discussed in this paper, which turns out to be a typical extremal problem located on the upper upper-high complex plane. If considering this in the Hardy space, the optimization operator of this problem will be highly simplified and an efficient algorithm is possible. This is mainly realized by the help of reproducing properties of the functions in the Hardy space of upper-high complex plane, and the detail algorithm is proposed. Moreover, harmonic mappings, which is a significant geometric transformation, are commonly used in many applications such as image processing, since it describes the energy minimization mappings between individual manifolds. Particularly, when we focus on the planer mappings between two Euclid planer regions, the harmonic mappings are exist and unique, which is guaranteed solidly by the existence of harmonic function. This property is attractive and simulation results are shown in this paper to ensure the capability of applications such as planer shape distortion and surface registration. |
| Tasks | |
| Published | 2017-05-24 |
| URL | http://arxiv.org/abs/1705.10596v1 |
| http://arxiv.org/pdf/1705.10596v1.pdf | |
| PWC | https://paperswithcode.com/paper/approximation-learning-methods-of-harmonic |
| Repo | |
| Framework | |
CNNComparator: Comparative Analytics of Convolutional Neural Networks
| Title | CNNComparator: Comparative Analytics of Convolutional Neural Networks |
| Authors | Haipeng Zeng, Hammad Haleem, Xavier Plantaz, Nan Cao, Huamin Qu |
| Abstract | Convolutional neural networks (CNNs) are widely used in many image recognition tasks due to their extraordinary performance. However, training a good CNN model can still be a challenging task. In a training process, a CNN model typically learns a large number of parameters over time, which usually results in different performance. Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations. In this paper, we present a visual analytics approach to compare two different snapshots of a trained CNN model taken after different numbers of epochs, so as to provide some insight into the design or the training of a better CNN model. Our system compares snapshots by exploring the differences in operation parameters and the corresponding blob data at different levels. A case study has been conducted to demonstrate the effectiveness of our system. |
| Tasks | |
| Published | 2017-10-15 |
| URL | http://arxiv.org/abs/1710.05285v1 |
| http://arxiv.org/pdf/1710.05285v1.pdf | |
| PWC | https://paperswithcode.com/paper/cnncomparator-comparative-analytics-of |
| Repo | |
| Framework | |
Polysemy Detection in Distributed Representation of Word Sense
| Title | Polysemy Detection in Distributed Representation of Word Sense |
| Authors | Kana Oomoto, Haruka Oikawa, Eiko Yamamoto, Mitsuo Yoshida, Masayuki Okabe, Kyoji Umemura |
| Abstract | In this paper, we propose a statistical test to determine whether a given word is used as a polysemic word or not. The statistic of the word in this test roughly corresponds to the fluctuation in the senses of the neighboring words a nd the word itself. Even though the sense of a word corresponds to a single vector, we discuss how polysemy of the words affects the position of vectors. Finally, we also explain the method to detect this effect. |
| Tasks | |
| Published | 2017-09-26 |
| URL | http://arxiv.org/abs/1709.08858v1 |
| http://arxiv.org/pdf/1709.08858v1.pdf | |
| PWC | https://paperswithcode.com/paper/polysemy-detection-in-distributed |
| Repo | |
| Framework | |
Recovery Guarantees for One-hidden-layer Neural Networks
| Title | Recovery Guarantees for One-hidden-layer Neural Networks |
| Authors | Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon |
| Abstract | In this paper, we consider regression problems with one-hidden-layer neural networks (1NNs). We distill some properties of activation functions that lead to $\mathit{local~strong~convexity}$ in the neighborhood of the ground-truth parameters for the 1NN squared-loss objective. Most popular nonlinear activation functions satisfy the distilled properties, including rectified linear units (ReLUs), leaky ReLUs, squared ReLUs and sigmoids. For activation functions that are also smooth, we show $\mathit{local~linear~convergence}$ guarantees of gradient descent under a resampling rule. For homogeneous activations, we show tensor methods are able to initialize the parameters to fall into the local strong convexity region. As a result, tensor initialization followed by gradient descent is guaranteed to recover the ground truth with sample complexity $ d \cdot \log(1/\epsilon) \cdot \mathrm{poly}(k,\lambda )$ and computational complexity $n\cdot d \cdot \mathrm{poly}(k,\lambda) $ for smooth homogeneous activations with high probability, where $d$ is the dimension of the input, $k$ ($k\leq d$) is the number of hidden nodes, $\lambda$ is a conditioning property of the ground-truth parameter matrix between the input layer and the hidden layer, $\epsilon$ is the targeted precision and $n$ is the number of samples. To the best of our knowledge, this is the first work that provides recovery guarantees for 1NNs with both sample complexity and computational complexity $\mathit{linear}$ in the input dimension and $\mathit{logarithmic}$ in the precision. |
| Tasks | |
| Published | 2017-06-10 |
| URL | http://arxiv.org/abs/1706.03175v1 |
| http://arxiv.org/pdf/1706.03175v1.pdf | |
| PWC | https://paperswithcode.com/paper/recovery-guarantees-for-one-hidden-layer |
| Repo | |
| Framework | |
Modulating and attending the source image during encoding improves Multimodal Translation
| Title | Modulating and attending the source image during encoding improves Multimodal Translation |
| Authors | Jean-Benoit Delbrouck, Stéphane Dupont |
| Abstract | We propose a new and fully end-to-end approach for multimodal translation where the source text encoder modulates the entire visual input processing using conditional batch normalization, in order to compute the most informative image features for our task. Additionally, we propose a new attention mechanism derived from this original idea, where the attention model for the visual input is conditioned on the source text encoder representations. In the paper, we detail our models as well as the image analysis pipeline. Finally, we report experimental results. They are, as far as we know, the new state of the art on three different test sets. |
| Tasks | |
| Published | 2017-12-09 |
| URL | http://arxiv.org/abs/1712.03449v1 |
| http://arxiv.org/pdf/1712.03449v1.pdf | |
| PWC | https://paperswithcode.com/paper/modulating-and-attending-the-source-image |
| Repo | |
| Framework | |
Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program
| Title | Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program |
| Authors | Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John Lee, Joshua Eckroth, Mark Crowley, Richard G. Freedman, Rogelio E. Cardona-Rivera, Tiago Machado, Tom Williams |
| Abstract | The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI’17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following “blue sky” questions: * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? * How could we teach AI topics at an early undergraduate or a secondary school level? * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields? This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education. |
| Tasks | |
| Published | 2017-02-01 |
| URL | http://arxiv.org/abs/1702.00137v1 |
| http://arxiv.org/pdf/1702.00137v1.pdf | |
| PWC | https://paperswithcode.com/paper/blue-sky-ideas-in-artificial-intelligence |
| Repo | |
| Framework | |
An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem
| Title | An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem |
| Authors | Sondre Glimsdal, Ole-Christoffer Granmo |
| Abstract | A number of intriguing decision scenarios revolve around partitioning a collection of objects to optimize some application specific objective function. This problem is generally referred to as the Object Partitioning Problem (OPP) and is known to be NP-hard. We here consider a particularly challenging version of OPP, namely, the Stochastic On-line Equi-Partitioning Problem (SO-EPP). In SO-EPP, the target partitioning is unknown and has to be inferred purely from observing an on-line sequence of object pairs. The paired objects belong to the same partition with probability $p$ and to different partitions with probability $1-p$, with $p$ also being unknown. As an additional complication, the partitions are required to be of equal cardinality. Previously, only sub-optimal solution strategies have been proposed for SO- EPP. In this paper, we propose the first optimal solution strategy. In brief, the scheme that we propose, BN-EPP, is founded on a Bayesian network representation of SO-EPP problems. Based on probabilistic reasoning, we are not only able to infer the underlying object partitioning with optimal accuracy. We are also able to simultaneously infer $p$, allowing us to accelerate learning as object pairs arrive. Furthermore, our scheme is the first to support arbitrary constraints on the partitioning (Constrained SO-EPP). Being optimal, BN-EPP provides superior performance compared to existing solution schemes. We additionally introduce Walk-BN-EPP, a novel WalkSAT inspired algorithm for solving large scale BN-EPP problems. Finally, we provide a BN-EPP based solution to the problem of order picking, a representative real-life application of BN-EPP. |
| Tasks | |
| Published | 2017-07-11 |
| URL | http://arxiv.org/abs/1707.03098v1 |
| http://arxiv.org/pdf/1707.03098v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-optimal-bayesian-network-based-solution |
| Repo | |
| Framework | |
RelNet: End-to-End Modeling of Entities & Relations
| Title | RelNet: End-to-End Modeling of Entities & Relations |
| Authors | Trapit Bansal, Arvind Neelakantan, Andrew McCallum |
| Abstract | We introduce RelNet: a new model for relational reasoning. RelNet is a memory augmented neural network which models entities as abstract memory slots and is equipped with an additional relational memory which models relations between all memory pairs. The model thus builds an abstract knowledge graph on the entities and relations present in a document which can then be used to answer questions about the document. It is trained end-to-end: only supervision to the model is in the form of correct answers to the questions. We test the model on the 20 bAbI question-answering tasks with 10k examples per task and find that it solves all the tasks with a mean error of 0.3%, achieving 0% error on 11 of the 20 tasks. |
| Tasks | Question Answering, Relational Reasoning |
| Published | 2017-06-22 |
| URL | http://arxiv.org/abs/1706.07179v2 |
| http://arxiv.org/pdf/1706.07179v2.pdf | |
| PWC | https://paperswithcode.com/paper/relnet-end-to-end-modeling-of-entities |
| Repo | |
| Framework | |
Realizing Half-Diminished Reality from Video Stream of Manipulating Objects
| Title | Realizing Half-Diminished Reality from Video Stream of Manipulating Objects |
| Authors | Hayato Okumoto, Mitsuo Yoshida, Kyoji Umemura |
| Abstract | When we watch a video, in which human hands manipulate objects, these hands may obscure some parts of those objects. We are willing to make clear how the objects are manipulated by making the image of hands semi-transparent, and showing the complete images of the hands and the object. By carefully choosing a Half-Diminished Reality method, this paper proposes a method that can process the video in real time and verifies that the proposed method works well. |
| Tasks | |
| Published | 2017-09-25 |
| URL | http://arxiv.org/abs/1709.08340v1 |
| http://arxiv.org/pdf/1709.08340v1.pdf | |
| PWC | https://paperswithcode.com/paper/realizing-half-diminished-reality-from-video |
| Repo | |
| Framework | |
