A BSTRACT We introduce and study the problem of Online Continual Compression, where one attempts to learn to compress and store a representative dataset from a non i.i.d data stream, while only observing each sample once. This problem is highly relevant for downstream online continual learning tasks, as well as standard learning methods under resource constrained data collection. To address this we propose a new architecture which Stacks Quantization Modules (SQM), consisting of a series of discrete autoencoders, each equipped with their own memory. Every added module is trained to reconstruct the latent space of the previous module using fewer bits, allowing the learned representation to become more compact as training progresses. This modularity has several advantages: 1) moderate compressions are quickly available early in training, which is crucial for remembering the early tasks, 2) as more data needs to be stored, earlier data becomes more compressed, freeing memory, 3) unlike previous methods, our approach does not require pretraining, even on challenging datasets. We show several potential applications of this method. We first replace the episodic memory used in Experience Replay with SQM, leading to significant gains on standard continual learning benchmarks using a fixed memory budget. We then apply our method to online compression of larger images like those from Imagenet, and show that it is also effective with other modalities, such as LiDAR data. 1 I NTRODUCTION Interest in machine learning in recent years has been fueled by the plethora of data being generated on a regular basis. Effectively storing and using this data is critical for many applications, especially those involving continual learning. In general, compression techniques can greatly improve data storage capacity, and, if done well, reduce the memory and compute usage in downstream machine learning tasks (Gueguen et al., 2018; Oyallon et al., 2018). Thus, learned compression has become a topic of great interest (Theis et al., 2017; Ball e et al., 2016; Johnston et al., 2018).

Zilong Zhao 1, Sophie Cerf 1, Bogdan Robu 1 and Nicolas Marchand 1 Abstract -- Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows. During their training, adaptation is often performed by tuning the learning rate. Usual learning rate strategies are time-based i.e. monotonously decreasing. In this paper, we advocate switching to a performance-based adaptation, in order to improve the learning efficiency. We present E (Exponential)/PD (Proportional Derivative)-Control, a conditional learning rate strategy that combines a feedback PD controller based on the CNN loss function, with an exponential control signal to smartly boost the learning and adapt the PD parameters. Stability proof is provided as well as an experimental evaluation using two state of the art image datasets (CIF AR-10 and Fashion-MNIST). Results show better performances than the related works (faster network accuracy growth reaching higher levels) and robustness of the E/PD-Control regarding its parametrization. I NTRODUCTION Convolutional neural networks (CNNs) are popular machine learning algorithms for image classification, as they are well suited for visual pattern recognition and require low preprocessing [1].

As the chief technology officer and assistant dean of the Stanford Graduate School of Education, Paul Kim spends more time than most pondering how artificial intelligence (AI) can impact education. He believes most educators don't think about it enough, and those who do worry too much about it. "We're at a very early stage of understanding what AI can possibly do for us, especially in the education teaching system," he says. "I think the possibilities are huge." One thing he doesn't see as a possibility?

Develop complete machine learning solutions in Python Write and test Python code interactively using Jupyter notebook Build, train, and test deep learning models using the popular Keras API Manipulate multidimensional data using NumPy Load and transform structured data using Pandas Build high quality, eye catching visualizations with Matplotlib Reduce training time using free Google Colab GPU instances in the cloud Recognize images using Convolutional Neural Networks (CNNs) Are you a developer interested in becoming a data scientist or machine learning engineer? Do you want to be proficient in the rapidly growing field of machine learning? One of the fastest and easiest ways to learn these skills is by example.

Zero-shot learning (ZSL) aims at understanding unseen categories with no training examples from class-level descriptions. To improve the discriminative power of zero-shot learning, we model the visual learning process of unseen categories with inspiration from the psychology of human creativity for producing novel art. We relate ZSL to human creativity by observing that zero-shot learning is about recognizing the unseen and creativity is about creating a likable unseen. We introduce a learning signal inspired by creativity literature that explores the unseen space with hallucinated class-descriptions and encourages careful deviation of their visual feature generations from seen classes while allowing knowledge transfer from seen to unseen classes. With hundreds of thousands of object categories in the real world and countless undiscovered species, it becomes unfeasible to maintain hundreds of examples per class to fuel the training needs of most existing recognition systems.

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

Are you an experienced Machine Learning / Computer Vision Engineer looking for a new opportunity? Vave Health is a startup based in the heart of Silicon Valley. Our mission is to provide the world with connected and personal tools that will help deliver better care, improve patient experience, and drive healthcare efficiency. Our next generation wireless connected device enables faster diagnosis and treatment at the point of care resulting in better patient outcomes. Vave Health is at the forefront of medical imaging and digital health.

We propose Neural Forest Learning (NFL), a novel deep learning based random-forest-like method. In contrast to previous forest methods, NFL enjoys the benefits of end-to-end, data-driven representation learning, as well as pervasive support from deep learning software and hardware platforms, hence achieving faster inference speed and higher accuracy than previous forest methods. Furthermore, NFL learns non-linear feature representations in CNNs more efficiently than previous higher-order pooling methods, producing good results with negligible increase in parameters, floating point operations (FLOPs) and real running time. We achieve superior performance on 7 machine learning datasets when compared to random forests and GBDTs. On the fine-grained benchmarks CUB-200-2011, FGVC-aircraft and Stanford Cars, we achieve over 5.7%, 6.9% and 7.8% gains for VGG-16, respectively. Moreover, NFL can converge in much fewer epochs, further accelerating network training. On the large-scale ImageNet ILSVRC-12 validation set, integration of NFL into ResNet-18 achieves top-1/top-5 errors of 28.32%/9.77%, which outperforms ResNet-18 by 1.92%/1.15% with negligible extra cost and the improvement is consistent under various architectures.

In order for an e-commerce platform to maximize its revenue, it must recommend customers items they are most likely to purchase. However, the company often has business constraints on these items, such as the number of each item in stock. In this work, our goal is to recommend items to users as they arrive on a webpage sequentially, in an online manner, in order to maximize reward for a company, but also satisfy budget constraints. We first approach the simpler online problem in which the customers arrive as a stationary Poisson process, and present an integrated algorithm that performs online optimization and online learning together. We then make the model more complicated but more realistic, treating the arrival processes as non-stationary Poisson processes. To deal with heterogeneous customer arrivals, we propose a time segmentation algorithm that converts a non-stationary problem into a series of stationary problems. Experiments conducted on large-scale synthetic data demonstrate the effectiveness and efficiency of our proposed approaches on solving constrained resource allocation problems.

I am working in ML/AI field for 6 years and apart from technical skills that I acquired while working on the projects, I have also discussed various aspects of ML/AI with my non-technical colleagues, who have mostly been senior manager, VPs or CXOs. When I heard about "AI For Everyone" course, I was a bit reluctant in attending it as I thought I know most of the generic stuff that might have been talked in the course. Recently, one of my colleagues discussed with me a few topics covered in this course which intrigued me to get a fresh perspective on these topics. So, I recently attended this course on Coursera. My motivation to write this blog is to make sure that I have understood key aspects of this course and am able to make my non-technical colleagues and project stakeholders understand the benefits & limitations of using AI.