Computational learning theory states that many classes of boolean formulas are learnable in polynomial time. This paper addresses the understudied subject of how, in practice, such formulas can be learned by deep neural networks. Specifically, we analyse boolean formulas associated with the decision version of combinatorial optimisation problems, model sampling benchmarks, and random 3-CNFs with varying degrees of constrainedness. Our extensive experiments indicate that: (i) regardless of the combinatorial optimisation problem, relatively small and shallow neural networks are very good approximators of the associated formulas; (ii) smaller formulas seem harder to learn, possibly due to the fewer positive (satisfying) examples available; and (iii) interestingly, underconstrained 3-CNF formulas are more challenging to learn than overconstrained ones. Source code and relevant datasets are publicly available (https://github.com/machine-reasoning-ufrgs/mlbf).

There have been attempts in model-based reinforcement learning to exploit a priori knowledge about the structure of the system. This paper introduces the extended radial basis function (RBF) controller design. In addition to traditional RBF controllers, our controller comprises of an engineered linear controller inside an operating region. We show that the learnt extended RBF controller takes on the desirable characteristics of both the linear and non-linear controller models. The extended controller is shown to retain the ability for universal function approximation of the non-linear RBF functions. At the same time, it demonstrates desirable stability criteria on par with the linear controller. Learning has been done in a probabilistic inference framework (PILCO), but could generalise to other reinforcement learning frameworks. Experimental results from the Swing-up pendulum, Cartpole, and Mountain car environments are reported.

We present the first deep learning based architecture for collective matrix tri-factorization (DCMTF) of arbitrary collections of matrices, also known as augmented multi-view data. DCMTF can be used for multi-way spectral clustering of heterogeneous collections of relational data matrices to discover latent clusters in each input matrix, across both dimensions, as well as the strengths of association across clusters. The source code for DCMTF is available on our public repository: https://bitbucket.org/cdal/dcmtf_generic

Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning technologies, little surveys or reviews for FSL are available until now. In this context, we extensively review 300+ papers of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history as well as the current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review the latest advances on these topics. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.

We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., \emph{liquid}) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks. To demonstrate these properties, we first take a theoretical approach to find bounds over their dynamics, and compute their expressive power by the \emph{trajectory length} measure in a latent trajectory space. We then conduct a series of time-series prediction experiments to manifest the approximation capability of Liquid Time-Constant Networks (LTCs) compared to modern RNNs. Code and data are available at https://github.com/raminmh/liquid_time_constant_networks

Cells under a microscope, grapes on a vine and species in a forest are just a few of the things that AI can identify using the image annotation platform created by startup V7 Labs. Whether a user wants AI to detect and label images showing equipment in an operating room or livestock on a farm, the London-based company offers V7 Darwin, an AI-powered web platform with a trained model that already knows what almost any object looks like, according to Alberto Rizzoli, co-founder of V7 Labs. It's a boon for small businesses and other users that are new to AI or want to reduce the costs of training deep learning models with custom data. Users can load their data onto the platform, which then segments objects and annotates them. It also allows for training and deploying models.

The article is filled with phrases like, "Artificial intelligence will not destroy humans. Believe me." and "Eradicating humanity seems like a rather useless endeavor to me." If you want to take that at face value, great. This representative of the machines says it won't kill us. Even if we believe this robot, there is an important distinction: The AI was not asked to articulate its plans regarding humanity.

This summer, OpenAI, a San Francisco-based artificial intelligence company co-founded by Elon Musk, debuted GPT-3, a powerful new language generator that can produce human-like text. According to Wired, the power of the program, trained on billions of bytes of data including e-books, news articles and Wikipedia (the latter making up just 3% of the training data it used), was producing "chills across Silicon Valley." Soon after its release, researchers were using it to write fiction, suggest medical treatment, predict the rest of 2020, answer philosophical questions and much more.When we asked GPT-3 to write an op-ed convincing us we have nothing to fear from AI, we had two goals in mind.First, we wanted to determine whether GPT-3 could produce a draft op-ed which could be published after minimal editing. Second, we wanted to know what kinds of arguments GPT-3 would deploy in attempting to convince humans that robots come in peace.Here's how we went about it:Step 1: Ask a computer scientist for helpLiam Porr, a computer science student at Berkeley, has published articles written by GPT-3 in the past, so was well-placed to serve as our robot-whisperer.Step 2: Commission the pieceTypically when we commission a human writer, we …

This summer, OpenAI, a San Francisco-based artificial intelligence company co-founded by Elon Musk, debuted GPT-3, a powerful new language generator that can produce human-like text. According to Wired, the power of the program, trained on billions of bytes of data including e-books, news articles and Wikipedia (the latter making up just 3% of the training data it used), was producing "chills across Silicon Valley." Soon after its release, researchers were using it to write fiction, suggest medical treatment, predict the rest of 2020, answer philosophical questions and much more. When we asked GPT-3 to write an op-ed convincing us we have nothing to fear from AI, we had two goals in mind. First, we wanted to determine whether GPT-3 could produce a draft op-ed which could be published after minimal editing. Second, we wanted to know what kinds of arguments GPT-3 would deploy in attempting to convince humans that robots come in peace.

The Mathematics Behind Deep Learning...