Many recent problems in signal processing and machine learning such as compressed sensing, image restoration, matrix/tensor recovery, and non-negative matrix factorization can be cast as constrained optimization. Projected gradient descent is a simple yet efficient method for solving such constrained optimization problems. Local convergence analysis furthers our understanding of its asymptotic behavior near the solution, offering sharper bounds on the convergence rate compared to global convergence analysis. However, local guarantees often appear scattered in problem-specific areas of machine learning and signal processing. This manuscript presents a unified framework for the local convergence analysis of projected gradient descent in the context of constrained least squares. The proposed analysis offers insights into pivotal local convergence properties such as the conditions for linear convergence, the region of convergence, the exact asymptotic rate of convergence, and the bound on the number of iterations needed to reach a certain level of accuracy. To demonstrate the applicability of the proposed approach, we present a recipe for the convergence analysis of projected gradient descent and demonstrate it via a beginning-to-end application of the recipe on four fundamental problems, namely, linear equality-constrained least squares, sparse recovery, least squares with the unit norm constraint, and matrix completion.

Design patterns are not just a way to structure code. They also communicate the problem addressed and how the code or component is intended to be used. Here are some patterns I've observed in machine learning code and systems, mostly from the Gang of Four design patterns book. Most developers have some familiarity with these patterns and having a basic understanding provides a shared vocabulary to discuss ideas on design and implementation. The factory pattern decouples objects, such as training data, from how they are created.

One of the purposes of the Tony Little Centre for Innovation and Research in Learning (CIRL) at Eton is to make sure that the school is keeping abreast of developments and innovations in education. Innovation typically seeds out of sight to meet a niche need; if it later finds a mainstream application, it is likely to be well-formed and possibly either a benefit or a potential threat. To future-proof our current success we need to be paying attention to the most significant innovations and to make sure that we see change coming. Artificial Intelligence (AI) is one such innovation. AI is already impacting on the kinds of careers many Etonians go into, including finance, medicine, law and the military.

The general aim of the course is to equip participants with practical and technical knowledge to deploy machine learning methods on genomic data sets. With this aim in mind, we will go through certain statistical concepts and move on to unsupervised and supervised machine learning methods to analyze high-dimensional data sets. There will be theoretical lectures followed by practical sessions where students directly apply what they have learned. These sessions will be provided online in succession. Participants will have a week to work on each module in their own time. Interactions will be provided over the online teaching platform.

Specifically, Huerta and his then graduate student Daniel George pioneered the use of so-called convolutional neural networks (CNNs), which are a type of deep-learning algorithm, to detect and decipher gravitational-wave signals in real time. Roughly speaking, training or teaching a deep-learning system involves feeding it data that are already categorized--say, images of galaxies obscured by lots of noise--and getting the network to identify the patterns in the data correctly. After their initial success with CNNs, Huerta and George, along with Huerta's graduate student Hongyu Shen, scaled up this effort, designing deep-learning algorithms that were trained on supercomputers using millions of simulated signatures of gravitational waves mixed in with noise derived from previous observing runs of Advanced LIGO--an upgrade to LIGO completed in 2015. For instance, Adam Rebei, a high school student in Huerta's group, showed in a recent study that deep learning can identify the complex gravitational-wave signals produced by the merger of black holes in eccentric orbits--something LIGO's traditional algorithms cannot do in real time. In a preprint paper last September, Nicholas Choma of New York University and his colleagues reported the development of a special type of deep-learning algorithm called a graph neural network, whose connections and architecture take advantage of the spatial geometry of the sensors in the ice and the fact that only a few sensors see the light from any given muon track.

Job summaryAre you passionate to help customers accelerate their journey with Machine Learning and Cloud Computing? AWS Machine Learning Product Management team is looking for an expert Machine Learning Architect with expertise in designing ML solutions to enable rapid adoption by customers. The ML Architect will be the Subject Matter Expert (SME) for helping enterprise customers design machine learning solutions that leverage the Amazon SageMaker on AWS. You will also partner with target ISV partners to develop deeper technical integration with Amazon SageMaker. You will partner with field SAs, Sales, Business Development and the ML Service teams to enable data migration and rapid adoption of Machine learning services. You will develop migration playbooks, reference implementations and share best practices with global community of ML specialists. You will have the opportunity to help shape and execute a strategy to build mindshare and broad use of AWS within startups and enterprise customers. The ideal candidate must be self-motivated with a proven track record in machine learning and solution architecture. You should be technically adept to complement customer teams in their adoption of AWS ML services. You should also have a demonstrated ability to think strategically about business, products, and technical challenges. Roles and Responsibilities · Work with customer’s ML team to deeply understand their business and technical needs and design ML solutions that make the best use of the AWS Cloud platform and ML services. · Partner with Generalist SAs, Sales, Business Development and the AI Service teams to accelerate customer adoption and revenue attainment. · Act as a technical liaison between customers and the service engineering teams to provide product enhancement feedback. · Develop and support an AWS internal community of AI related subject matter experts. · Capture and share best-practice knowledge amongst the AWS solutions architect community · Build deep relationships with senior technical individuals within customers to enable them to be cloud advocates · Act as a technical liaison between customers, service engineering teams and support · Thought Leadership – Evangelize AWS Services and share best practices through forums such as AWS blogs, whitepapers, reference architectures and public-speaking events such as AWS Summit, AWS re:Invent, etc. · Open to domestic travel up to 30%Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.Amazon is an Equal Opportunity Employer – Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.

Spiking neural networks are efficient computation models for low-power environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful techniques for SNN training. Nevertheless, the spike-base BP training is slow and requires large memory costs. Though ANN2NN provides a low-cost way to train SNNs, it requires many inference steps to mimic the well-trained ANN for good performance. In this paper, we propose a SNN-to-ANN (SNN2ANN) framework to train the SNN in a fast and memory-efficient way. The SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN and SNN and b) spiking mapping units. Firstly, the architecture trains the weight-sharing parameters on the ANN branch, resulting in fast training and low memory costs for SNN. Secondly, the spiking mapping units ensure that the activation values of the ANN are the spiking features. As a result, the classification error of the SNN can be optimized by training the ANN branch. Besides, we design an adaptive threshold adjustment (ATA) algorithm to address the noisy spike problem. Experiment results show that our SNN2ANN-based models perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet). Moreover, the SNN2ANN can achieve comparable accuracy under 0.625x time steps, 0.377x training time, 0.27x GPU memory costs, and 0.33x spike activities of the Spike-based BP model.

Inductive transfer learning aims to learn from a small amount of training data for the target task by utilizing a pre-trained model from the source task. Most strategies that involve large-scale deep learning models adopt initialization with the pre-trained model and fine-tuning for the target task. However, when using over-parameterized models, we can often prune the model without sacrificing the accuracy of the source task. This motivates us to adopt model pruning for transfer learning with deep learning models. In this paper, we propose PAC-Net, a simple yet effective approach for transfer learning based on pruning. PAC-Net consists of three steps: Prune, Allocate, and Calibrate (PAC). The main idea behind these steps is to identify essential weights for the source task, fine-tune on the source task by updating the essential weights, and then calibrate on the target task by updating the remaining redundant weights. Under the various and extensive set of inductive transfer learning experiments, we show that our method achieves state-of-the-art performance by a large margin.

Intel, in association with Analytics India Magazine, is organising a webinar on "achieving real-time AI inference on your CPU" on 7th July, from 5:00 – 6:30 PM (IST). We all know that the amount of data generated in today's world is exponential. AI Inference involves the process of using a trained neural network model to predict an outcome. For a typical AI workflow, the workloads associated with all the steps involved follow a diverse mechanism and a single GPU or CPU cannot work for the entire pipeline smoothly. To this end, Intel is organising this webinar for the attendees to understand how to optimise a deep learning neural network model and achieve fast AI inference with a CPU.