Material recognition enables robots to incorporate knowledge of material properties into their interactions with everyday objects. For example, material recognition opens up opportunities for clearer communication with a robot, such as "bring me the metal coffee mug", and recognizing plastic versus metal is crucial when using a microwave or oven. However, collecting labeled training data with a robot is often more difficult than unlabeled data. We present a semi-supervised learning approach for material recognition that uses generative adversarial networks (GANs) with haptic features such as force, temperature, and vibration. Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled. We explore how well this approach can recognize the material of new objects and we discuss challenges facing generalization. To motivate learning from unlabeled training data, we also compare results against several common supervised learning classifiers. In addition, we have released the dataset used for this work which consists of time-series haptic measurements from a robot that conducted thousands of interactions with 72 household objects.

I had the opportunity the other week to spend a couple of hours shooting the breeze with our lead data scientist Tijl Carpels. While we chatted over an overpriced macchiato on everything from Star Wars to crypto currency the talk very quickly settled on the use of supervised and unsupervised algorithms in AI. This all came about because we have launched a new bespoke AI product (ISAAC) and I really wanted to know more about how it worked and also why did we choose this path. So the theory is simple, but as many have experienced putting this into practice is fraught with dangers. The problem a lot of us have is that in the mid 90's many companies were sold'black box' platforms that promised a lot and delivered little.

We investigate a family of regression problems in a semi-supervised setting. The task is to assign real-valued labels to a set of $n$ sample points, provided a small training subset of $N$ labeled points. A goal of semi-supervised learning is to take advantage of the (geometric) structure provided by the large number of unlabeled data when assigning labels. We consider random geometric graphs, with connection radius $\epsilon(n)$, to represent the geometry of the data set. Functionals which model the task reward the regularity of the estimator function and impose or reward the agreement with the training data. Here we consider the discrete $p$-Laplacian regularization. We investigate asymptotic behavior when the number of unlabeled points increases, while the number of training points remains fixed. We uncover a delicate interplay between the regularizing nature of the functionals considered and the nonlocality inherent to the graph constructions. We rigorously obtain almost optimal ranges on the scaling of $\epsilon(n)$ for the asymptotic consistency to hold. We prove that the minimizers of the discrete functionals in random setting converge uniformly to the desired continuum limit. Furthermore we discover that for the standard model used there is a restrictive upper bound on how quickly $\epsilon(n)$ must converge to zero as $n \to \infty$. We introduce a new model which is as simple as the original model, but overcomes this restriction.

In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work has managed to learn semi-supervised models in a non-restrictive regime. However, so far such approaches have only been proposed for linear models. In this work, we introduce semi-supervised parameter learning for Sum-Product Networks (SPNs). SPNs are deep probabilistic models admitting inference in linear time in number of network edges. Our approach has several advantages, as it (1) allows generative and discriminative semi-supervised learning, (2) guarantees that adding unlabelled data can increase, but not degrade, the performance (safe), and (3) is computationally efficient and does not enforce restrictive assumptions on the data distribution. We show on a variety of data sets that safe semi-supervised learning with SPNs is competitive compared to state-of-the-art and can lead to a better generative and discriminative objective value than a purely supervised approach.

This is the first in a series of posts about machine learning concepts, where we'll cover everything from learning styles to new dimensions in machine learning research. What makes machine learning so successful? The answer lies in the core concept of machine learning: a machine can learn from examples and experience. Before machine learning, machines were programmed with specific instructions and had no need to learn on their own. A machine (without machine learning) is born knowing exactly what it's supposed to do and how to do it, like a robot arm on an assembly line. The problem with this approach, as Erik Brynjolfsson and Andrew McAfee put so well, is that "we humans know more than we can tell."

Honestly, your results look awesome and the idea is simple yet effective and can be applied in addition to many other GAN tricks. If only the presentation were more readable, you would get way more attention from this subreddit.

Generative modeling, a typical unsupervised learning that makes use of huge amount of unlabeled data, lies in the heart of rapid development of modern machine learning techniques [1]. Different from discriminative tasks such as pattern recognition, the goal of generative modeling is to model the probability distribution of input data and thus be able to generate new samples according to the distribution. At the research frontier of generative modeling, it was used for finding good data representation and dealing with tasks with missing data. Popular generative machine learning models include the Boltzmann Machines (BM) [2, 3] and their generalizations [4], variational autoencoders (VAE) [5], autoregressive models [6, 7], nonlinear density estimations [8-10], and the generative adversarial networks (GAN) [11]. For generative model design, one tries to balance the representational power and efficiency of learning and sampling. There is a long history of relation between generative modeling and physics, especially statistical physics. Some celebrated models, such as Hopfield model [12], and Boltzmann machine [2, 3], are closely related to the Ising model in statistical physics, and its inverse version which learns couplings in the Ising model based on given training configurations [13, 14]. The task of generative modeling also shares many similarities with quantum physics research in the sense that both of them try to model probability distributions in an enormously large space. In the past decades, tensor network (TN) states and algorithms have been shown to be an incredibly potent tool set for studying many-body quantum physics with its power in expressing quantum states relevant to realistic situations [15, 16].

There are valuable insights buried within your data, but they can be virtually impossible to find manually. Every time a data scientist spends hours immersed in data, wrangling and tweaking a mathematical code or script fragments, the dream of data science and machine learning bringing agility to your organization seems to retreat. Manual data science for industrial processes can be extremely counter-productive, especially when businesses embracing the IIoT are greatly emphasizing superior dexterity in operations. Any opportunity which can speed up delivery and output should be seized by every aspiring industrial manufacturer. One of the biggest challenges for the industrial digital enterprise is extracting precise outcomes by channeling huge volumes of data into meaningful information, and then performing accurate analyses to streamline business processes.

In the last instalment of this blog series, we discussed objectives and accuracy in machine learning. And we described two crucial tests for the utility of a machine learning model: The model must be sufficiently accurate and we must be able to deploy the model so that it can produce actionable outputs from the available data. We then introduced a real-world scenario -- predicting train failures up to 36 hours in advance of their occurrence using sensor data -- to illustrate the application of those tests. But how did we decide which of the multitude of machine learning algorithms to use to train our model in the first place? To answer this question, we need to revisit the main classes of machine learning algorithms.

In this paper, we consider the interpretability of the foundational Laplacian-based semi-supervised learning approaches on graphs. We introduce a novel flow-based learning framework that subsumes the foundational approaches and additionally provides a detailed, transparent, and easily understood expression of the learning process in terms of graph flows. As a result, one can visualize and interactively explore the precise subgraph along which the information from labeled nodes flows to an unlabeled node of interest. Surprisingly, the proposed framework avoids trading accuracy for interpretability, but in fact leads to improved prediction accuracy, which is supported both by theoretical considerations and empirical results. The flow-based framework guarantees the maximum principle by construction and can handle directed graphs in an out-of-the-box manner.