Deep Ensembles: A Loss Landscape Perspective: This paper takes a dig into why the ensemble of deep networks works better than a single deep network. The authors did a qualitative investigation that actually demystifies some of the inner workings of deep neural nets. These are some of the observation: Same model trained with different initial initializations is functionally dissimilar. Neural networks map inputs to outputs and thus act as a function(which we learn obviously). If we start with init1 we end up with function1 which is not similar to the same model trained with init2. However, if we take a snapshot of the model at different epochs they are functionally similar.

The Machine Learning Courses report also focuses on the comprehensive study of the industry environment, and industry chain structure extensively.

In recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based on deep learning techniques which are fine-tuned to specific domains, are difficult to interpret and produce results which are not machine readable. This limits their applicability and adoption as they can only be used by a select expert audience in very specific settings. In this paper we propose an architecture based on a core concept of Credibility Reviews (CRs) that can be used to build networks of distributed bots that collaborate for misinformation detection. The CRs serve as building blocks to compose graphs of (i) web content, (ii) existing credibility signals --fact-checked claims and reputation reviews of websites--, and (iii) automatically computed reviews. We implement this architecture on top of lightweight extensions to Schema.org and services providing generic NLP tasks for semantic similarity and stance detection. Evaluations on existing datasets of social-media posts, fake news and political speeches demonstrates several advantages over existing systems: extensibility, domain-independence, composability, explainability and transparency via provenance. Furthermore, we obtain competitive results without requiring finetuning and establish a new state of the art on the Clef'18 CheckThat! Factuality task.

The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space that is learned in an unsupervised manner. In the original VAE model, input data vectors are processed independently. In the recent years, a series of papers have presented different extensions of the VAE to sequential data, that not only model the latent space, but also model the temporal dependencies within a sequence of data vectors and/or corresponding latent vectors, relying on recurrent neural networks or state space models. In this paper we perform an extensive literature review of these models. Importantly, we introduce and discuss a general class of models called Dynamical Variational Autoencoders (DVAEs) that encompass a large subset of these temporal VAE extensions. Then we present in details seven different instances of DVAE that were recently proposed in the literature, with an effort to homogenize the notations and presentation lines, as well as to relate those models with existing classical temporal models (that are also presented for the sake of completeness). We reimplemented those seven DVAE models and we present the results of an experimental benchmark that we conducted on the speech analysis-resynthesis task (the PyTorch code will be made publicly available). An extensive discussion is presented at the end of the paper, aiming to comment on important issues concerning the DVAE class of models and to describe future research guidelines.

Sparse rewards present a difficult problem in reinforcement learning and may be inevitable in certain domains with complex dynamics such as real-world robotics. Hindsight Experience Replay (HER) is a recent replay memory development that allows agents to learn in sparse settings by altering memories to show them as successful even though they may not be. While, empirically, HER has shown some success, it does not provide guarantees around the makeup of samples drawn from an agent's replay memory. This may result in minibatches that contain only memories with zero-valued rewards or agents learning an undesirable policy that completes HER-adjusted goals instead of the actual goal. In this paper, we introduce Or Your Money Back (OYMB), a replay memory sampler designed to work with HER. OYMB improves training efficiency in sparse settings by providing a direct interface to the agent's replay memory that allows for control over minibatch makeup, as well as a preferential lookup scheme that prioritizes real-goal memories before HER-adjusted memories. We test our approach on five tasks across three unique environments. Our results show that using HER in combination with OYMB outperforms using HER alone and leads to agents that learn to complete the real goal more quickly.

For decades, writers and filmmakers have dreamt of the AI revolution. Whether it's the evil HAL 9000 from 2001: A Space Odyssey or the sentient droids of Star Wars, complex artificial intelligence is a hallmark in the depiction of advanced futuristic civilizations. But, in reality, how close are we to an AI revolution? When will we be able to reap the rewards of computerized thought? Will we ever see the intelligence that matches our wildest imagination in science-fiction?

An artist has transformed the chipped stone busts of ancient Roman emperors into photorealistic portraits with the help of historical artefacts and creative software. Daniel Voshart, from Toronto, Canada, says that his project of painstakingly colourising and shaping the faces of 54 Principate rulers was'a quarantine project that got a bit out of hand', but it has attracted attention from hobbyists to historians. And he has now released his completed work in a series of stunning portraits and posters that cover 300 years of Roman history. Though more interested in design work for VR for use in architecture and the film industry, the coronavirus pandemic brought Daniel's work to stop and left him with time to explore his hobby of colourising statues. When he came to pick a subject however, he chose to research the busts of Roman Emperors who controlled its sprawling empire during the first three-century-long Principate, despite not being particularly interested in ancient history.

Each of the cards have mechanics that are inspired by either the characters or the elements of The Walking Dead universe that they represent,

The recent report on “Global Artificial intelligence (AI) Chips Market Report 2020 by Key Players, Types, Applications, Countries, Market Size, Forecast …

In addition, the University of Illinois at Urbana-Champaign will receive federal funding for two research institutes focused on artificial intelligence.