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In recent years, technology has played a significant role in reshaping the landscape of college teaching, and it will surely continue to do so. But the groundswell of artificial intelligence (AI) that surrounds us marks a particularly fragile moment for teaching. In this context, educators must be especially mindful that our uses of technology do not undermine meaningful learning. And doing this requires knowledge about technology and teaching. That's because for many students, college is a pathway to prepare for the workforce or improve one's existing skills to advance a career.

The holy grail in deep neural network research is porting the memory- and computation-intensive network models on embedded platforms with a minimal compromise in model accuracy. To this end, we propose a novel approach, termed as Variational Student, where we reap the benefits of compressibility of the knowledge distillation (KD) framework, and sparsity inducing abilities of variational inference (VI) techniques. Essentially, we build a sparse student network, whose sparsity is induced by the variational parameters found via optimizing a loss function based on VI, leveraging the knowledge learnt by an accurate but complex pre-trained teacher network. Further, for sparsity enhancement, we also employ a Block Sparse Regularizer on a concatenated tensor of teacher and student network weights. We demonstrate that the marriage of KD and the VI techniques inherits compression properties from the KD framework, and enhances levels of sparsity from the VI approach, with minimal compromise in the model accuracy. We benchmark our results on LeNet MLP and VGGNet (CNN) and illustrate a memory footprint reduction of 64x and 213x on these MLP and CNN variants, respectively, without a need to retrain the teacher network. Furthermore, in the low data regime, we observed that our method outperforms state-of-the-art Bayesian techniques in terms of accuracy.

When the cost of an operational test is expensive, it is desirable to Nagoya Institute of Technology † RIKEN Center for Advanced Intelligence Project ‡ National Institute for Materials Sciences § email:takeuchi.ichiro@nitech.ac.jp be able to identify the region of appropriate input conditions in as few operational tests as possible. If we regard the operational conditions as inputs and the results of the operational tests as outputs of a black-box function, this problem can be viewed as a type of active learning (AL) problem called Level Set Estimation (LSE) . LSE is defined as the problem of identifying the input region in which the outputs of a function are smaller/greater than a certain threshold. In the statistics and machine learning literature, many methods for the LSE problem have been proposed [Bryan et al., 2006, Gotovos et al., 2013, Zanette et al., 2018]. In practical manufacturing applications, since it is often difficult to accurately control the input conditions during the actual usage of the machine, there is a need to guarantee the performance of the machine after properly incorporating the possible variation of input conditions.

A BSTRACT Generative Adversarial Networks (GANs) are known to be difficult to train, despite considerable research effort. Several regularization techniques for stabilizing training have been proposed, but they introduce nontrivial computational overheads and interact poorly with existing techniques like spectral normalization. In this work, we propose a simple, effective training stabilizer based on the notion of consistency regularization--a popular technique in the semi-supervised learning literature. In particular, we augment data passing into the GAN discriminator and penalize the sensitivity of the discriminator to these augmentations. We conduct a series of experiments to demonstrate that consistency regularization works effectively with spectral normalization and various GAN architectures, loss functions and optimizer settings. Our method achieves the best FID scores for unconditional image generation compared to other regularization methods on CIFAR-10 and CelebA. In the original setting, GANs are composed of two neural networks trained with competing goals: the generator is trained to synthesize realistic samples to fool the discriminator and the discriminator is trained to distinguish real samples from fake ones produced by the generator. One major problem with GANs is the instability of the training procedure and the general sensitivity of the results to various hyperparameters (Salimans et al., 2016).

Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning. When providing expert demonstrations to human students, we know that the demonstrations must fall within a particular range of difficulties called the "Zone of Proximal Development (ZPD)". If they are too easy the student learns nothing, but if they are too difficult the student is unable to follow along. This raises the question: Given a set of potential demonstrators, which among them is best suited for teaching any particular learner? Prior work, such as the popular Deep Q-learning from Demonstrations (DQfD) algorithm has generally focused on single demonstrators. In this work we consider the problem of choosing among multiple demonstrators of varying skill levels. Our results align with intuition from human learners: it is not always the best policy to draw demonstrations from the best performing demonstrator (in terms of reward). We show that careful selection of teaching strategies can result in sample efficiency gains in the learner's environment across nine Atari games

Jupyter Notebook is the new excel. It is the workhorse of modern data science workflows. It allows data engineers to analyze and present data in a visual and interactive format. A reminder to paid subscribers: check the website frequently for updates.

A major challenge for any company that is starting out in the realm of data-driven markets is the deployment of machine learning pipelines at full scale for their products. To tap the most out of AI, it is necessary to build service-specific tools and frameworks in addition to the existing models. The best strategy varies from product to product; but the rubrics of machine learning stay the same. To democratise the use of machine learning, Google has condensed their years of research into a paper titled "A Rubric for ML Production Readiness", where they listed out their findings in the form of 28 specific tests that have shown promising results. The offline/online metric relationship can be measured in one or more small scale A/B experiments using an intentionally degraded model.

Machine learning is a branch of Artificial Intelligence (AI) that presents systems with the ability to learn automatically to increase their accuracy without being programmed. The primary aim is to enable the machine systems to learn on their own, without any form of human intervention. Even though most people must have heard about it, only a few fully understand what it is and its benefits to eLearning. There are many benefits of machine learning for online training. However, one needs to make use of the best practices to achieve the benefits and deliver a better Learning Experience.

The Machine Learning Center at Georgia Tech (ML@GT) is home to many talented students from across campus, representing all six of Georgia Tech's colleges and the Georgia Tech Research Institute (GTRI). These students have diverse backgrounds and a wide variety of interests both inside and outside of the classroom. Today, we'd like you to meet James Smith, a second-year machine learning Ph.D. student. Smith is a unique combination of athlete and academic; he runs at least one marathon each year while also working on ways to design machine learning algorithms that positively impact the world. Other degrees earned and from what institution: B.S. and M.S. in Electrical Engineering, both from Auburn University (War Eagle!) Tell us about your research interests. Where might people be impacted by them in everyday life?