With the enormous growth of data size n and model complexity, asynchronous parallel algorithms [1, 2, 3, 4, 5, 6] have become an important tool and received significant successes for solving large scale machine learning problems in the form of (1). Asynchronous parallel algorithms distribute computation on multicore systems (shared memory architecture) or multi-machine system (parameter server architecture), whose computation power generally scales up with the increasing number of cores or machines. As a consequence, effective design and implementation of asynchronous parallel algorithms is critical for large scale machine learning. Numerous efforts have been devoted to this topic. Among them, asynchronous stochastic gradient descent is proposed in [1, 2], and its performance is guaranteed by theoretical convergence analyses. An asynchronous proximal gradient descent algorithm is designed on the parameter server architecture in [3] with a distributed optimization software provided. Convergence rate of asynchronous stochastic gradient descent with a nonconvex objective is analyzed in [4].

Dimensionality reduction techniques play an essential role in data analytics, signal processing and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations that are well-suited for the intended task is more appealing. This paper proposes a joint factor analysis and latent clustering framework, which aims at learning cluster-aware low-dimensional representations of matrix and tensor data. The proposed approach leverages matrix and tensor factorization models that produce essentially unique latent representations of the data to unravel latent cluster structure -- which is otherwise obscured because of the freedom to apply an oblique transformation in latent space. At the same time, latent cluster structure is used as prior information to enhance the performance of factorization. Specific contributions include several custom-built problem formulations, corresponding algorithms, and discussion of associated convergence properties. Besides extensive simulations, real-world datasets such as Reuters document data and MNIST image data are also employed to showcase the effectiveness of the proposed approaches.

Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis.

Machine Learning (ML) services are quickly becoming a taken-for-granted part of the software developer's toolbox, in any domain. These days, databases or networking are a standard component of almost any non-trivial application, so easily integrated that almost no special expertise is required. We expect to see Machine Learning becoming, in the very near future, a similar layer in the software stack. This commoditization of ML services has been driven so far by Service-oriented platforms such as BigML, which have provided a key ingredient of the process: abstraction. Simple and easy to use REST APIs hide away not only the details of the sophisticated algorithms underlying the services at hand, but also the complexities of scaling those computations both over CPU cycles and input data volumes.

Transition Between The Current Market And Where The Market Is Going 1.3.3

ICRA is almost over, and we hope you've been enjoying our coverage, which so far has featured robot moths, zipper actuators, machine learning, and duckies. We'll have lots more from the converence over the next few weeks, but for you impatient types, we're cramming Video Friday this week with a painstakingly curated selection of ICRA videos--emphasis on pain: there were nearly 500 videos! We tried to include videos from many different areas of robotics: control, sensing, humanoids, actuators, exoskeletons, manipulators, prosthetics, aerial vehicles, grasping, AI, VR, haptics, vision, and microrobots. We're posting the abstracts along with the videos, but if you have any questions about these projects, let us know and we'll get more details from the authors. Have a great weekend everyone! We present an adaptive filter model of cerebellar function applied to the calibration of a tactile sensory map to improve the accuracy of directed movements of a robotic manipulator.

This repository provides a logistic regression implementation in python for our fair classification mechanism introduced in (Zafar et al., 2016). Please cite the paper when using the code. Fair classification corresponds to a scenario where we are learning classifiers from a dataset that is biased towards/against a specific demographic group, yet the classifier predictions are fair and do not show the biases contained in the data. For more details, have a look at Section 2 of our paper. Lets start off by generating a sample dataset where class labels are biased towards a certain group.

Google CEO Sundar Pichai said on Alphabet's Q1 earnings call: "In the long run, we will evolve in computing from a mobile-first to an AI-first world". This has prompted various speculation on what an AI-first world will look like. Pichai envisages that it will include "assistive" search, "especially on mobile," suggesting that artificial intelligence (AI) will be the platform for on-demand services accessed from any device – including smartphones. Dave Coplin, chief envisioning officer at Microsoft UK spoke at the AI Summit in London. He believes that AI first (AI as a platform) will "change how people relate to tech and to each other."

Lawyers, doctors and accountants may be redundant in 20 years after scientists have claimed their jobs will be taken over by robots . A study into the future of human employment has predicted a surge in machine-led work such as robotic counsellors, body part makers and virtual lawyers. This is bad news for those in the profession, who could see themselves out of a job due to highly-skilled artificial intelligence. The worrying research suggests that humans will be replaced because robots are able to produce better results. A report compiled by professor of management practice at London Business School, Lynda Gratton, and futurologist David A. Smith, looked at different sector jobs.

The rise of artificial intelligence could have a more anticlimactic outcome than most doomsday films would have you expect. Rather than being violently wiped out by robotic beings, humankind may become'eternally useless' due to the increasing capabilities of AI. This is according to bestselling author Yuval Noah Harari, who explores bleak future of humanity and'the rise of the useless class' in his upcoming novel Homo Deus: A Brief History of Tomorrow. The rise of artificial intelligence could have a more anticlimactic outcome than most doomsday films would have you expect. Rather than being violently wiped out by robotic beings, the increasing capabilities of AI may instead render humankind'eternally useless.'