OpenRaiser/Intern-Atlas huggingface dataset:
Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars
Authors: Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, Chong Wang
Venue: Conference KDD 2014
Year: 2014

Recommendation and review sites offer a wealth of information beyond ratings. For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their reviews. This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings. The ability to answer questions such as "Does this user care more about the plot or about the special effects?" or "What is the quality of the movie in terms of acting?" helps us to understand why certain ratings are generated. This can be used to provide more meaningful recommendations. In this work we propose a probabilistic model based on collaborative filtering and topic modeling. It allows us to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on a per-aspect basis. Unlike prior work our approach is entirely unsupervised and does not require knowledge of the aspect specific ratings or genres for inference. We evaluate our model on a live copy crawled from IMDb. Our model offers superior performance by joint modeling. Moreover, we are able to address the cold start problem -- by utilizing the information inherent in reviews our model demonstrates improvement for new users and movies.
Samplernn: An unconditional end-to-end neural audio generation model
Authors: Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron C. Courville, Yoshua Bengio
Venue: Conference ICLR 2017
Year: 2017

In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.
Robot trajectory optimization using approximate inference
Authors: Marc Toussaint
Venue: Conference ICML 2009
Year: 2009

The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to first compute an optimal (deterministic) trajectory and then solve a local linear-quadratic-gaussian (LQG) perturbation model to handle the system stochasticity. We present a new algorithm for this approach which improves upon previous algorithms like iLQG. We consider a probabilistic model for which the maximum likelihood (ML) trajectory coincides with the optimal trajectory and which, in the LQG case, reproduces the classical SOC solution. The algorithm then utilizes approximate inference methods (similar to expectation propagation) that efficiently generalize to non-LQG systems. We demonstrate the algorithm on a simulated 39-DoF humanoid robot.
Generative models and model criticism via optimized maximum mean discrepancy
Authors: Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
Venue: Conference ICLR 2017
Year: 2017

We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial networks (GAN), in which a model attempts to generate realistic samples, and a discriminator attempts to tell these apart from data samples. In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples. Second, the MMD can be used to evaluate the performance of a generative model, by testing the model's samples against a reference data set. In the latter role, the optimized MMD is particularly helpful, as it gives an interpretable indication of how the model and data distributions differ, even in cases where individual model samples are not easily distinguished either by eye or by classifier.
Parametrized hierarchical procedures for neural programming
Authors: Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, Ion Stoica
Venue: Conference ICLR 2018
Year: 2018

Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism. Despite the potential to increase the interpretability and the compositionality of the behavior of artificial agents, it remains difficult to learn from demonstrations neural networks that represent computer programs. The main challenges that set algorithmic domains apart from other imitation learning domains are the need for high accuracy, the involvement of specific structures of data, and the extremely limited observability. To address these challenges, we propose to model programs as Parametrized Hierarchical Procedures (PHPs). A PHP is a sequence of conditional operations, that uses a program counter, along with the observation, to select between taking an elementary action, invoking another PHP as a sub-procedure, and returning to the caller. We develop an algorithm for training PHPs from a mixture of annotated and unannotated demonstrations, and apply it to efficient level-wise training of multi-level PHPs. We show in two benchmarks, NanoCraft and long-hand addition, that PHPs can learn neural programs more accurately from smaller amounts of strong and weak supervision.
Capturing image structure with probabilistic index maps
Authors: Nebojsa Jojic, Yaron Caspi
Venue: Conference CVPR 2004
Year: 2004

One of the major problems in modeling images for vision tasks is that images with very similar structure may locally have completely different appearance, e.g., images taken under different illumination conditions, or the images of pedestrians with different clothing. While there have been many successful attempts to address these problems in application-specific settings, we believe that underlying a large set of problems in vision is a representational deficiency of intensity-derived local measurements that are the basis of most efficient models. We argue that interesting structure in images is better captured when the image is defined as a matrix whose entries are discrete indices to a separate palette of possible intensities, colors or other features, much like the image representation often used to save on storage. In order to model the variability in images, we define an image class not by a single index map, but by a probability distribution over the index maps, which can be automatically estimated from the data, and which we call probabilistic index maps. The existing algorithms can be adapted to work with this representation, as we illustrate in this paper on the example of transformation-invariant clustering and background subtraction. Furthermore, the probabilistic index map representation leads to algorithms with computational costs proportional to either the size of the palette or the log of the size of the palette, making the cost of significantly increased invariance to non-structural changes quite bearable.
Stel component analysis: Modeling spatial correlations in image class structure
Authors: Nebojsa Jojic, Alessandro Perina, Marco Cristani, Vittorio Murino, Brendan J. Frey
Venue: Conference CVPR 2009
Year: 2009

As a useful concept in the study of the low level image class structure, we introduce the notion of a structure element - `stel.' The notion is related to the notions of a pixel, superpixel, segment or a part, but instead of referring to an element or a region of a single image, stel is a probabilistic element of an entire image class. Stels often define clear object or scene parts as a consequence of the modeling constraint which forces the regions belonging to a single stel to have a tight distribution over local measurements, such as color or texture. This self-similarity within a region in a single image is typical of most meaningful image parts, even when in different images of similar objects the corresponding parts may not have similar local measurements. The stel itself is expected to be consistent within a class, yet flexible, which we accomplish using a novel approach we dubbed stel component analysis. Experimental results show how stel component analysis can assist in image/video segmentation and object recognition where, in particular, it can be used as an alternative of, or in conjunction with, bag-of-features and related classifiers, where stel inference provides a meaningful spatial partition of features.
Active neural localization
Authors: Devendra Singh Chaplot, Emilio Parisotto, Ruslan Salakhutdinov
Venue: Conference ICLR 2018
Year: 2018

Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to localize accurately while minimizing the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.
Chainer: A deep learning framework for accelerating the research cycle
Authors: Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, Hiroyuki Yamazaki Vincent
Venue: Conference KDD 2019
Year: 2019

Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.
Discovering additive structure in black box functions
Authors: Giles Hooker
Venue: Conference KDD 2004
Year: 2004

Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that drive it. A common approach to interpretation is to plot the dependence of a learned function on one or two predictors. We present a method that seeks not to display the behavior of a function, but to evaluate the importance of nonadditive interactions within any set of variables. Should the function be close to a sum of low dimensional components, these components can be viewed and even modeled parametrically. Alternatively, the work here provides an indication of where intrinsically high-dimensional behavior takes place. The calculations used in this paper correspond closely with the functional ANOVA decomposition; a well-developed construction in Statistics. In particular, the proposed score of interaction importance measures the loss associated with the projection of the prediction function onto a space of additive models. The algorithm runs in linear time and we present displays of the output as a graphical model of the function for interpretation purposes.
Factorized sparse learning models with interpretable high order feature interactions
Authors: Sanjay Purushotham, Martin Renqiang Min, C.-C. Jay Kuo, Rachel Ostroff
Venue: Conference KDD 2014
Year: 2014

Identifying interpretable discriminative high-order feature interactions given limited training data in high dimensions is challenging in both machine learning and data mining. In this paper, we propose a factorization based sparse learning framework termed FHIM for identifying high-order feature interactions in linear and logistic regression models, and study several optimization methods for solving them. Unlike previous sparse learning methods, our model FHIM recovers both the main effects and the interaction terms accurately without imposing tree-structured hierarchical constraints. Furthermore, we show that FHIM has oracle properties when extended to generalized linear regression models with pairwise interactions. Experiments on simulated data show that FHIM outperforms the state-of-the-art sparse lear-ning techniques. Further experiments on our experimentally generated data from patient blood samples using a novel SOMAmer (Slow Off-rate Modified Aptamer) technology show that, FHIM performs blood-based cancer diagnosis and bio-marker discovery for Renal Cell Carcinoma much better than other competing methods, and it identifies interpretable block-wise high-order gene interactions predictive of cancer stages of samples. A literature survey shows that the interactions identified by FHIM play important roles in cancer development.
Detecting statistical interactions with additive groves of trees
Authors: Daria Sorokina, Rich Caruana, Mirek Riedewald, Daniel Fink 0002
Venue: Conference ICML 2008
Year: 2008

Discovering additive structure is an important step towards understanding a complex multi-dimensional function because it allows the function to be expressed as the sum of lower-dimensional components. When variables interact, however, their effects are not additive and must be modeled and interpreted simultaneously. We present a new approach for the problem of interaction detection. Our method is based on comparing the performance of unrestricted and restricted prediction models, where restricted models are prevented from modeling an interaction in question. We show that an additive model-based regression ensemble, Additive Groves, can be restricted appropriately for use with this framework, and thus has the right properties for accurately detecting variable interactions.
Graph representation learning via hard and channel-wise attention networks
Authors: Hongyang Gao, Shuiwang Ji
Venue: Conference KDD 2019
Year: 2019

Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes. However, graph attention operators (GAOs) consume excessive computational resources, preventing their applications on large graphs. In addition, GAOs belong to the family of soft attention, instead of hard attention, which has been shown to yield better performance. In this work, we propose novel hard graph attention operator~(hGAO) and channel-wise graph attention operator~(cGAO). hGAO uses the hard attention mechanism by attending to only important nodes. Compared to GAO, hGAO improves performance and saves computational cost by only attending to important nodes. To further reduce the requirements on computational resources, we propose the cGAO that performs attention operations along channels. cGAO avoids the dependency on the adjacency matrix, leading to dramatic reductions in computational resource requirements. Experimental results demonstrate that our proposed deep models with the new operators achieve consistently better performance. Comparison results also indicates that hGAO achieves significantly better performance than GAO on both node and graph embedding tasks. Efficiency comparison shows that our cGAO leads to dramatic savings in computational resources, making them applicable to large graphs.
Tractable nonparametric bayesian inference in poisson processes with gaussian process intensities
Authors: Ryan Prescott Adams, Iain Murray 0001, David J. C. MacKay
Venue: Conference ICML 2009
Year: 2009

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.
Distributed large-scale natural graph factorization
Authors: Amr Ahmed, Nino Shervashidze, Shravan M. Narayanamurthy, Vanja Josifovski, Alexander J. Smola
Venue: Conference WWW 2013
Year: 2013

Natural graphs, such as social networks, email graphs, or instant messaging patterns, have become pervasive through the internet. These graphs are massive, often containing hundreds of millions of nodes and billions of edges. While some theoretical models have been proposed to study such graphs, their analysis is still difficult due to the scale and nature of the data.
Block-distributed gradient boosted trees
Authors: Theodore Vasiloudis, Hyunsu Cho, Henrik Boström
Venue: Conference SIGIR 2019
Year: 2019

The Gradient Boosted Tree (GBT) algorithm is one of the most popular machine learning algorithms used in production, for tasks that include Click-Through Rate (CTR) prediction and learning-to-rank. To deal with the massive datasets available today, many distributed GBT methods have been proposed. However, they all assume a row-distributed dataset, addressing scalability only with respect to the number of data points and not the number of features, and increasing communication cost for high-dimensional data. In order to allow for scalability across both the data point and feature dimensions, and reduce communication cost, we propose block-distributed GBTs. We achieve communication efficiency by making full use of the data sparsity and adapting the Quickscorer algorithm to the block-distributed setting. We evaluate our approach using datasets with millions of features, and demonstrate that we are able to achieve multiple orders of magnitude reduction in communication cost for sparse data, with no loss in accuracy, while providing a more scalable design. As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.
Stochastic gradient boosted distributed decision trees
Authors: Jerry Ye, Jyh-Herng Chow, Jiang Chen, Zhaohui Zheng
Venue: Conference CIKM 2009
Year: 2009

Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today. It is adaptable, easy to interpret, and produces highly accurate models. However, most implementations today are computationally expensive and require all training data to be in main memory. As training data becomes ever larger, there is motivation for us to parallelize the GBDT algorithm. Parallelizing decision tree training is intuitive and various approaches have been explored in existing literature. Stochastic boosting on the other hand is inherently a sequential process and have not been applied to distributed decision trees. In this work, we present two different distributed methods that generates exact stochastic GBDT models, the first is a MapReduce implementation and the second utilizes MPI on the Hadoop grid environment.
Estimating local intrinsic dimensionality
Authors: Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stéphane Girard, Michael E. Houle, Ken-ichi Kawarabayashi, Michael Nett
Venue: Conference KDD 2015
Year: 2015

This paper is concerned with the estimation of a local measure of intrinsic dimensionality (ID) recently proposed by Houle. The local model can be regarded as an extension of Karger and Ruhl's expansion dimension to a statistical setting in which the distribution of distances to a query point is modeled in terms of a continuous random variable. This form of intrinsic dimensionality can be particularly useful in search, classification, outlier detection, and other contexts in machine learning, databases, and data mining, as it has been shown to be equivalent to a measure of the discriminative power of similarity functions. Several estimators of local ID are proposed and analyzed based on extreme value theory, using maximum likelihood estimation (MLE), the method of moments (MoM), probability weighted moments (PWM), and regularly varying functions (RV). An experimental evaluation is also provided, using both real and artificial data.
Composite templates for cloth modeling and sketching
Authors: Hong Chen, Zi Jian Xu 0001, Ziqiang Liu, Song Chun Zhu
Venue: Conference CVPR 2006
Year: 2006

Cloth modeling and recognition is an important and challenging problem in both vision and graphics tasks, such as dressed human recognition and tracking, human sketch and portrait. In this paper, we present a context sensitive grammar in an And-Or graph representation which will produce a large set of composite graphical templates to account for the wide variabilities of cloth configurations, such as T-shirts, jackets, etc. In a supervised learning phase, we ask an artist to draw sketches on a set of dressed people, and we decompose the sketches into categories of cloth and body components: collars, shoulders, cuff, hands, pants, shoes etc. Each component has a number of distinct subtemplates (sub-graphs). These sub-templates serve as leafnodes in a big And-Or graph where an And-node represents a decomposition of the graph into sub-configurations with Markov relations for context and constraints (soft or hard), and an Or-node is a switch for choosing one out of a set of alternative And-nodes (sub-configurations) - similar to a node in stochastic context free grammar (SCFG). This representation integrates the SCFG for structural variability and the Markov (graphical) model for context. An algorithm which integrates the bottom-up proposals and the topdown information is proposed to infer the composite cloth template from the image.