Vishvas Vasuki

Bio: Vishvas Vasuki is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Graphical model & Recommender system. The author has an hindex of 4, co-authored 4 publications receiving 217 citations.

On Learning Discrete Graphical Models using Group-Sparse Regularization

Abstract: We study the problem of learning the graph structure associated with a general discrete graphical models (each variable can take any of m > 1 values, the clique factors have maximum size c ≥ 2) from samples, under high-dimensional scaling where the number of variables p could be larger than the number of samples n. We provide a quantitative consistency analysis of a procedure based on node-wise multi-class logistic regression with group-sparse regularization. We first consider general m-ary pairwise models – where each factor depends on at most two variables. We show that when the number of samples scale as n > K(m − 1) 2 d 2 log((m −1) 2 (p −1))– where d is the maximum degree and K a fixed constant – the procedure succeeds in recovering the graph with high probability. For general models with c-way factors, the natural multi-way extension of the pairwise method quickly becomes very computationally complex. So we studied the effectiveness of using the pairwise method even while the true model has higher order factors. Surprisingly, we show that under slightly more stringent conditions, the pairwise procedure still recovers the graph structure, when the samples scale as n > K(m − 1) 2 d 3 2 c 1 log((m − 1) c (p − 1) c 1 ).

Scalable Affiliation Recommendation using Auxiliary Networks

Abstract: Social network analysis has attracted increasing attention in recent years In many social networks, besides friendship links among users, the phenomenon of users associating themselves with groups or communities is common Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups In this article, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks More generally, affiliations need not be community affiliations---they can be a user’s taste, so affiliation recommendation algorithms have applications beyond community recommendation In this article, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities We explore the affiliation recommendation algorithms suggested by these models and evaluate these algorithms on two real-world networks, Orkut and Youtube In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top 50 recommendations are for the average user, and demonstrate the importance of choosing the right evaluation strategy The algorithms suggested by the graph proximity model turn out to be the most effective We also introduce scalable versions of these algorithms, and demonstrate their effectiveness This use of link prediction techniques for the purpose of affiliation recommendation is, to our knowledge, novel

Affiliation recommendation using auxiliary networks

Abstract: Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links amongst users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In this paper, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks. More generally, affiliations need not be community affiliations - they can be a user's taste, so affiliation recommendation algorithms have applications beyond community recommendation. In this paper, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations. Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities. We explore the two classes of affiliation recommendation algorithms suggested by these models. We evaluate these algorithms on two real world networks - Orkut and Youtube. In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top 50 recommendations are for the average user, and demonstrate the importance of choosing the right evaluation strategy. The algorithms suggested by the graph proximity model turn out to be the most effective and efficient. This use of link prediction techniques for the purpose of affiliation recommendation is, to our knowledge, novel.

Wireless sensor networks for human intruder detection

Abstract: In this paper we report on the outcomes of a research and demonstration project on human intrusion detection in a large secure space using an ad hoc wireless sensor network. This project has been ∗SmartDetect is a research and demonstration project funded by the ERI (the following are in alphabetical order within category) Faculty Investigators: Bharadwaj Amrutur, G.K. Ananthasuresh, Navakanta Bhat, R.C. Hansdah, Malati Hegde, Joy Kuri, Vinod Sharma, Y.N. Srikant, Rajesh Sundaresan; Project Staff: Tarun Agarwal, S.V.R. Anand, Pallav Bose, Vijay Dewangan, Shalini Keshavamurthy, A.V. Krishna, Pavan Kumar, Sharath Kumar, D. Manjunath, Sundeep Patil, Poornima V.L., K. Aditya Prasad, Santosh Ramanathan, Anurag Ranjan, Subathra Sampath, Jeena Sebastian, Vishwas Vasuki; Students: Pranav Agrawal, Sahebrao Sidram Baiger, Vinod Kumar Chouhan, Taposh Banerjee, Satyam Dwivedi, Abhishek Gupta, Neeraj Kumar, Prachee Jindal, Premkumar Karumbu, Sambuddha Khan, Girish Krishnan, Syam Krishnan, Chaitanya U. Kshirasagar, K.P. Naveen, Mohan Rathod, Deepak Ravi, U. Raviteja, R. Abu Sajana, Ramanathan Subramanian, Thejas, Amulya Ratna Swain, Lalitha Vadlamani, Leena Zacharias. a unique experience in collaborative research, involving ten investigators (with expertise in areas such as sensors, circuits, computer systems, communication and networking, signal processing and security) to execute a large funded project that spanned three to four years. In this paper we report on the specific engineering solution that was developed: the various architectural choices and the associated specific designs. In addition to developing a demonstrable system, the various problems that arose have given rise to a large amount of basic research in areas such as geographical packet routing, distributed statistical detection, sensors and associated circuits, a low power adaptive micro-radio, and power optimising embedded systems software. We provide an overview of the research results obtained.

Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

Abstract: Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

Social recommendation: a review

Abstract: Recommender systems play an important role in helping online users find relevant information by suggesting information of potential interest to them Due to the potential value of social relations in recommender systems, social recommendation has attracted increasing attention in recent years In this paper, we present a review of existing recommender systems and discuss some research directions We begin by giving formal definitions of social recommendation and discuss the unique property of social recommendation and its implications compared with those of traditional recommender systems Then, we classify existing social recommender systems into memory-based social recommender systems and model-based social recommender systems, according to the basic models adopted to build the systems, and review representative systems for each category We also present some key findings from both positive and negative experiences in building social recommender systems, and research directions to improve social recommendation capabilities

Fast ConvNets Using Group-wise Brain Damage

Abstract: We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The suggested brain damage process prunes the convolutional kernel tensor in a group-wise fashion by adding group-sparsity regularization to the standard training process. After such group-wise pruning, convolutions can be reduced to multiplications of thinned dense matrices, which leads to speedup. In the comparison on AlexNet, the method achieves very competitive performance.

Fast ConvNets Using Group-Wise Brain Damage

Abstract: We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers in ConvNets. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The suggested brain damage process prunes the convolutional kernel tensor in a group-wise fashion. After such pruning, convolutions can be reduced to multiplications of thinned dense matrices, which leads to speedup. We investigate different ways to add group-wise prunning to the learning process, and show that severalfold speedups of convolutional layers can be attained using group-sparsity regularizers. Our approach can adjust the shapes of the receptive fields in the convolutional layers, and even prune excessive feature maps from ConvNets, all in data-driven way.