Paper Group ANR 179
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips. Simple epistemic planning: generalised gossiping. Neural Discourse Modeling of Conversations. Low-complexity Image and Video Coding Based on an Approximate Discrete Tchebichef Transform. A Differentiable Transition Between Additive and Multiplicati …
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips
| Title | From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips |
| Authors | Aditya Singh, Saurabh Saini, Rajvi Shah, P J Narayanan |
| Abstract | Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilise semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlablled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilise a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance. |
| Tasks | Action Classification, Data Augmentation, Domain Adaptation |
| Published | 2016-10-18 |
| URL | http://arxiv.org/abs/1610.05613v1 |
| http://arxiv.org/pdf/1610.05613v1.pdf | |
| PWC | https://paperswithcode.com/paper/from-traditional-to-modern-domain-adaptation |
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Simple epistemic planning: generalised gossiping
| Title | Simple epistemic planning: generalised gossiping |
| Authors | Martin C. Cooper, Andreas Herzig, Faustine Maffre, Frédéric Maris, Pierre Régnier |
| Abstract | The gossip problem, in which information (known as secrets) must be shared among a certain number of agents using the minimum number of calls, is of interest in the conception of communication networks and protocols. We extend the gossip problem to arbitrary epistemic depths. For example, we may require not only that all agents know all secrets but also that all agents know that all agents know all secrets. We give optimal protocols for various versions of the generalised gossip problem, depending on the graph of communication links, in the case of two-way communications, one-way communications and parallel communication. We also study different variants which allow us to impose negative goals such as that certain agents must not know certain secrets. We show that in the presence of negative goals testing the existence of a successful protocol is NP-complete whereas this is always polynomial-time in the case of purely positive goals. |
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| Published | 2016-06-10 |
| URL | http://arxiv.org/abs/1606.03244v2 |
| http://arxiv.org/pdf/1606.03244v2.pdf | |
| PWC | https://paperswithcode.com/paper/simple-epistemic-planning-generalised |
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Neural Discourse Modeling of Conversations
| Title | Neural Discourse Modeling of Conversations |
| Authors | John M. Pierre, Mark Butler, Jacob Portnoff, Luis Aguilar |
| Abstract | Deep neural networks have shown recent promise in many language-related tasks such as the modeling of conversations. We extend RNN-based sequence to sequence models to capture the long range discourse across many turns of conversation. We perform a sensitivity analysis on how much additional context affects performance, and provide quantitative and qualitative evidence that these models are able to capture discourse relationships across multiple utterances. Our results quantifies how adding an additional RNN layer for modeling discourse improves the quality of output utterances and providing more of the previous conversation as input also improves performance. By searching the generated outputs for specific discourse markers we show how neural discourse models can exhibit increased coherence and cohesion in conversations. |
| Tasks | |
| Published | 2016-07-15 |
| URL | http://arxiv.org/abs/1607.04576v1 |
| http://arxiv.org/pdf/1607.04576v1.pdf | |
| PWC | https://paperswithcode.com/paper/neural-discourse-modeling-of-conversations |
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Low-complexity Image and Video Coding Based on an Approximate Discrete Tchebichef Transform
| Title | Low-complexity Image and Video Coding Based on an Approximate Discrete Tchebichef Transform |
| Authors | P. A. M. Oliveira, R. J. Cintra, F. M. Bayer, S. Kulasekera, A. Madanayake, V. A. Coutinho |
| Abstract | The usage of linear transformations has great relevance for data decorrelation applications, like image and video compression. In that sense, the discrete Tchebichef transform (DTT) possesses useful coding and decorrelation properties. The DTT transform kernel does not depend on the input data and fast algorithms can be developed to real time applications. However, the DTT fast algorithm presented in literature possess high computational complexity. In this work, we introduce a new low-complexity approximation for the DTT. The fast algorithm of the proposed transform is multiplication-free and requires a reduced number of additions and bit-shifting operations. Image and video compression simulations in popular standards shows good performance of the proposed transform. Regarding hardware resource consumption for FPGA shows 43.1% reduction of configurable logic blocks and ASIC place and route realization shows 57.7% reduction in the area-time figure when compared with the 2-D version of the exact DTT. |
| Tasks | Video Compression |
| Published | 2016-09-24 |
| URL | https://arxiv.org/abs/1609.07630v3 |
| https://arxiv.org/pdf/1609.07630v3.pdf | |
| PWC | https://paperswithcode.com/paper/low-complexity-image-and-video-coding-based |
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A Differentiable Transition Between Additive and Multiplicative Neurons
| Title | A Differentiable Transition Between Additive and Multiplicative Neurons |
| Authors | Wiebke Köpp, Patrick van der Smagt, Sebastian Urban |
| Abstract | Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function a neuron should perform. However, this leads to an extensive increase in the computational complexity of the training procedure. We present a novel, parameterizable transfer function based on the mathematical concept of non-integer functional iteration that allows the operation each neuron performs to be smoothly and, most importantly, differentiablely adjusted between addition and multiplication. This allows the decision between addition and multiplication to be integrated into the standard backpropagation training procedure. |
| Tasks | |
| Published | 2016-04-13 |
| URL | http://arxiv.org/abs/1604.03736v1 |
| http://arxiv.org/pdf/1604.03736v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-differentiable-transition-between-additive |
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TweeTime: A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter
| Title | TweeTime: A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter |
| Authors | Jeniya Tabassum, Alan Ritter, Wei Xu |
| Abstract | We describe TweeTIME, a temporal tagger for recognizing and normalizing time expressions in Twitter. Most previous work in social media analysis has to rely on temporal resolvers that are designed for well-edited text, and therefore suffer from the reduced performance due to domain mismatch. We present a minimally supervised method that learns from large quantities of unlabeled data and requires no hand-engineered rules or hand-annotated training corpora. TweeTIME achieves 0.68 F1 score on the end-to-end task of resolving date expressions, outperforming a broad range of state-of-the-art systems. |
| Tasks | |
| Published | 2016-08-09 |
| URL | http://arxiv.org/abs/1608.02904v3 |
| http://arxiv.org/pdf/1608.02904v3.pdf | |
| PWC | https://paperswithcode.com/paper/tweetime-a-minimally-supervised-method-for |
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Decision Tree Classification on Outsourced Data
| Title | Decision Tree Classification on Outsourced Data |
| Authors | Koray Mancuhan, Chris Clifton |
| Abstract | This paper proposes a client-server decision tree learning method for outsourced private data. The privacy model is anatomization/fragmentation: the server sees data values, but the link between sensitive and identifying information is encrypted with a key known only to clients. Clients have limited processing and storage capability. Both sensitive and identifying information thus are stored on the server. The approach presented also retains most processing at the server, and client-side processing is amortized over predictions made by the clients. Experiments on various datasets show that the method produces decision trees approaching the accuracy of a non-private decision tree, while substantially reducing the client’s computing resource requirements. |
| Tasks | |
| Published | 2016-10-18 |
| URL | http://arxiv.org/abs/1610.05796v1 |
| http://arxiv.org/pdf/1610.05796v1.pdf | |
| PWC | https://paperswithcode.com/paper/decision-tree-classification-on-outsourced |
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Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation
| Title | Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation |
| Authors | Weinan Zhang, Lingxi Chen, Jun Wang |
| Abstract | User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users’ interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user’s interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users’ ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines. |
| Tasks | Transfer Learning |
| Published | 2016-01-11 |
| URL | http://arxiv.org/abs/1601.02377v1 |
| http://arxiv.org/pdf/1601.02377v1.pdf | |
| PWC | https://paperswithcode.com/paper/implicit-look-alike-modelling-in-display-ads |
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Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
| Title | Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups |
| Authors | Chi Xu, Lakshmi Narasimhan Govindarajan, Yu Zhang, Li Cheng |
| Abstract | Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately. In this paper, a unified paradigm based on Lie group theory is proposed, which enables us to collectively address these related problems. Our approach is also applicable to a wide range of articulated objects. Empirically it is evaluated on lab animals including mouse and fish, as well as on human hand. On these applications, it is shown to deliver competitive results compared to the state-of-the-arts, and non-trivial baselines including convolutional neural networks and regression forest methods. |
| Tasks | Pose Estimation, Temporal Action Localization |
| Published | 2016-09-13 |
| URL | http://arxiv.org/abs/1609.03773v1 |
| http://arxiv.org/pdf/1609.03773v1.pdf | |
| PWC | https://paperswithcode.com/paper/lie-x-depth-image-based-articulated-object |
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Dilemma First Search for Effortless Optimization of NP-Hard Problems
| Title | Dilemma First Search for Effortless Optimization of NP-Hard Problems |
| Authors | Julien Weissenberg, Hayko Riemenschneider, Ralf Dragon, Luc Van Gool |
| Abstract | To tackle the exponentiality associated with NP-hard problems, two paradigms have been proposed. First, Branch & Bound, like Dynamic Programming, achieve efficient exact inference but requires extensive information and analysis about the problem at hand. Second, meta-heuristics are easier to implement but comparatively inefficient. As a result, a number of problems have been left unoptimized and plain greedy solutions are used. We introduce a theoretical framework and propose a powerful yet simple search method called Dilemma First Search (DFS). DFS exploits the decision heuristic needed for the greedy solution for further optimization. DFS is useful when it is hard to design efficient exact inference. We evaluate DFS on two problems: First, the Knapsack problem, for which efficient algorithms exist, serves as a toy example. Second, Decision Tree inference, where state-of-the-art algorithms rely on the greedy or randomness-based solutions. We further show that decision trees benefit from optimizations that are performed in a fraction of the iterations required by a random-based search. |
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| Published | 2016-09-12 |
| URL | http://arxiv.org/abs/1609.03545v1 |
| http://arxiv.org/pdf/1609.03545v1.pdf | |
| PWC | https://paperswithcode.com/paper/dilemma-first-search-for-effortless |
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Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits
| Title | Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits |
| Authors | Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz |
| Abstract | We study a generalization of the multi-armed bandit problem with multiple plays where there is a cost associated with pulling each arm and the agent has a budget at each time that dictates how much she can expect to spend. We derive an asymptotic regret lower bound for any uniformly efficient algorithm in our setting. We then study a variant of Thompson sampling for Bernoulli rewards and a variant of KL-UCB for both single-parameter exponential families and bounded, finitely supported rewards. We show these algorithms are asymptotically optimal, both in rateand leading problem-dependent constants, including in the thick margin setting where multiple arms fall on the decision boundary. |
| Tasks | |
| Published | 2016-06-30 |
| URL | https://arxiv.org/abs/1606.09388v3 |
| https://arxiv.org/pdf/1606.09388v3.pdf | |
| PWC | https://paperswithcode.com/paper/asymptotically-optimal-algorithms-for |
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Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets
| Title | Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets |
| Authors | Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique del Castillo |
| Abstract | We present a new method for estimating multivariate, second-order stationary Gaussian Random Field (GRF) models based on the Sparse Precision matrix Selection (SPS) algorithm, proposed by Davanloo et al. (2015) for estimating scalar GRF models. Theoretical convergence rates for the estimated between-response covariance matrix and for the estimated parameters of the underlying spatial correlation function are established. Numerical tests using simulated and real datasets validate our theoretical findings. Data segmentation is used to handle large data sets. |
| Tasks | |
| Published | 2016-05-11 |
| URL | http://arxiv.org/abs/1605.03267v2 |
| http://arxiv.org/pdf/1605.03267v2.pdf | |
| PWC | https://paperswithcode.com/paper/generalized-sparse-precision-matrix-selection |
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Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees
| Title | Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees |
| Authors | Xin Mu, Kai Ming Ting, Zhi-Hua Zhou |
| Abstract | This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The SENC problem can be decomposed into three sub problems: detecting emerging new classes, classifying for known classes, and updating models to enable classification of instances of the new class and detection of more emerging new classes. The proposed method employs completely random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. This is the first time, as far as we know, that completely random trees are used as a single common core to solve all three sub problems: unsupervised learning, supervised learning and model update in data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods. |
| Tasks | |
| Published | 2016-05-30 |
| URL | http://arxiv.org/abs/1605.09131v1 |
| http://arxiv.org/pdf/1605.09131v1.pdf | |
| PWC | https://paperswithcode.com/paper/classification-under-streaming-emerging-new |
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Online Adaptation of Deep Architectures with Reinforcement Learning
| Title | Online Adaptation of Deep Architectures with Reinforcement Learning |
| Authors | Thushan Ganegedara, Lionel Ott, Fabio Ramos |
| Abstract | Online learning has become crucial to many problems in machine learning. As more data is collected sequentially, quickly adapting to changes in the data distribution can offer several competitive advantages such as avoiding loss of prior knowledge and more efficient learning. However, adaptation to changes in the data distribution (also known as covariate shift) needs to be performed without compromising past knowledge already built in into the model to cope with voluminous and dynamic data. In this paper, we propose an online stacked Denoising Autoencoder whose structure is adapted through reinforcement learning. Our algorithm forces the network to exploit and explore favourable architectures employing an estimated utility function that maximises the accuracy of an unseen validation sequence. Different actions, such as Pool, Increment and Merge are available to modify the structure of the network. As we observe through a series of experiments, our approach is more responsive, robust, and principled than its counterparts for non-stationary as well as stationary data distributions. Experimental results indicate that our algorithm performs better at preserving gained prior knowledge and responding to changes in the data distribution. |
| Tasks | Denoising |
| Published | 2016-08-08 |
| URL | http://arxiv.org/abs/1608.02292v1 |
| http://arxiv.org/pdf/1608.02292v1.pdf | |
| PWC | https://paperswithcode.com/paper/online-adaptation-of-deep-architectures-with |
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Clustering Time-Series Energy Data from Smart Meters
| Title | Clustering Time-Series Energy Data from Smart Meters |
| Authors | Alexander Lavin, Diego Klabjan |
| Abstract | Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs. |
| Tasks | Time Series |
| Published | 2016-03-24 |
| URL | http://arxiv.org/abs/1603.07602v1 |
| http://arxiv.org/pdf/1603.07602v1.pdf | |
| PWC | https://paperswithcode.com/paper/clustering-time-series-energy-data-from-smart |
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