In my opinion it is everyone's own judgement. In this case, try model with and without intercept. The model which will predict more accurate, select that one. It is not mandatory to include intercept.

Williams and Beer (2010) proposed a nonnegative mutual information decomposition, based on the construction of redundancy lattices, which allows separating the information that a set of variables contains about a target variable into nonnegative components interpretable as the unique information of some variables not contained in others as well as redundant and synergistic components. However, the definition of multivariate measures of redundancy that comply with nonnegativity and conform to certain axioms that capture conceptually desirable properties of redundancy has proven to be elusive. We here present a procedure to determine multivariate redundancy measures, within the framework of maximum entropy models. In particular, we generalize existing bivariate maximum entropy-based measures of redundancy and unique information, defining measures of the redundant information that a group of variables has about a target, and of the unique redundant information that a group of variables has about a target that is not redundant with information from another group. The two key ingredients for this approach are: First, the identification of a type of constraints on entropy maximization that allows isolating components of redundancy and unique redundancy by mirroring them to synergy components. Second, the construction of rooted tree-based decompositions to breakdown mutual information, ensuring nonnegativity by the local implementation of maximum entropy information projections at each binary unfolding of the tree nodes. Altogether, the proposed measures are nonnegative and conform to the desirable axioms for redundancy measures.

The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is important, it is also helpful for novices to develop an intuitive feel for the effect of different hyperparameters and structural variations. We describe TensorFlow Playground, an interactive, open sourced visualization that allows users to experiment via direct manipulation rather than coding, enabling them to quickly build an intuition about neural nets.

This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple stochastic functional constraints that are i.i.d. generated at each round and are disclosed to the decision maker only after the decision is made. This formulation arises naturally when decisions are restricted by stochastic environments or deterministic environments with noisy observations. It also includes many important problems as special cases, such as OCO with long term constraints, stochastic constrained convex optimization, and deterministic constrained convex optimization. To solve this problem, this paper proposes a new algorithm that achieves $O(\sqrt{T})$ expected regret and constraint violations and $O(\sqrt{T}\log(T))$ high probability regret and constraint violations. Experiments on a real-world data center scheduling problem further verify the performance of the new algorithm.

Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases. However, due to the requirement of discrete outputs for the hash functions, learning such functions is known to be very challenging. In addition, the objective functions adopted by existing hashing techniques are mostly chosen heuristically. In this paper, we propose a novel generative approach to learn hash functions through Minimum Description Length principle such that the learned hash codes maximally compress the dataset and can also be used to regenerate the inputs. We also develop an efficient learning algorithm based on the stochastic distributional gradient, which avoids the notorious difficulty caused by binary output constraints, to jointly optimize the parameters of the hash function and the associated generative model. Extensive experiments on a variety of large-scale datasets show that the proposed method achieves better retrieval results than the existing state-of-the-art methods.

The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions. The suggested architecture is tested on task data set of Human Connectome Project (HCP) where we extract multi-resolution networks, each of which corresponds to a cognitive task. At the first level of this architecture, we decompose the fMRI signal into multiple sub-bands using wavelet decompositions. At the second level, for each sub-band, we estimate a brain network extracted from short time windows of the fMRI signal. At the third level, we feed the adjacency matrices of each mesh network at each time-resolution into an unsupervised deep learning algorithm, namely, a Stacked De- noising Auto-Encoder (SDAE). The outputs of the SDAE provide a compact connectivity representation for each time window at each sub-band of the fMRI signal. We concatenate the learned representations of all sub-bands at each window and cluster them by a hierarchical algorithm to find the natural groupings among the windows. We observe that each cluster represents a cognitive task with a performance of 93% Rand Index and 71% Adjusted Rand Index. We visualize the mean values and the precisions of the networks at each component of the cluster mixture. The mean brain networks at cluster centers show the variations among cognitive tasks and the precision of each cluster shows the within cluster variability of networks, across the subjects.

In this paper we present UbuntuWorld 1.0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system. Specifically, we propose to use the Bash terminal as a simulator of the Ubuntu environment for a learning-based agent, and demonstrate the usefulness of adopting reinforcement learning (RL) techniques for basic problem solving and troubleshooting in this environment. We provide a plug-and-play interface to the simulator as a python package where different types of agents can be plugged in and evaluated, and provide pathways for integrating data from online support forums like Ask Ubuntu into an automated agent's learning process. Finally, we show that the use of this data significantly improves the agent's learning efficiency. We believe that this platform can be adopted as a real-world test bed for research on automated technical support.

Machine Learning manuscript No. (will be inserted by the editor) Abstract One of the fundamental problems in crowdsourcing is the tradeoff between the number of the workers needed for high-accuracy aggregation and the budget to pay. For saving budget, it is important to ensure high quality of the crowd-sourced labels, hence the total cost on label collection will be reduced. Since the self-confidence of the workers often has a close relationship with their abilities, a possible way for quality control is to request the workers to return the labels only when they feel confident, by means of providing unsure option to them. On the other hand, allowing workers to choose unsure option also leads to the potential danger of budget waste. In this work, we propose the analysis towards understanding when providing the unsure option indeed leads to significant cost reduction, as well as how the confidence threshold is set. We also propose an online mechanism, which is alternative for threshold selection when the estimation of the crowd ability distribution is difficult. Keywords Crowdsourcing · Mechanism design · Unsure option · Cost reduction 1 Introduction Labeled data play a crucial role in machine learning. In recent years, crowdsourcing has been a popular cost-saving way for label collection.

Students at John Burroughs High School will soon learn how to create and develop their own video games for computers and smartphone apps using a new textbook. The textbook is "Learn Mobile Game Development in One Day Using GameSalad" by Jamie Cross, a Pennsylvania-based computer programmer, and it received approval Thursday night from the Burbank Unified school board. Thirty-six copies will be purchased at $36.99 each for a total of roughly $1,500, according to a staff report. Students enrolled in the introductory computer course called Digital Applications will use the textbook as supplemental material to the GameSalad software, a coding program that uses a "drag-and-drop platform" to teach the fundamentals of computer science. The material is geared toward students who lack prior game-development experience.