Generative adversarial training for imitation learning has shown promising results on high-dimensional and continuous control tasks. This paradigm is based on reducing the imitation learning problem to the density matching problem, where the agent iteratively refines the policy to match the empirical state-action visitation frequency of the expert demonstration. Although this approach has shown to robustly learn to imitate even with scarce demonstration, one must still address the inherent challenge that collecting trajectory samples in each iteration is a costly operation. To address this issue, we first propose a Bayesian formulation of generative adversarial imitation learning (GAIL), where the imitation policy and the cost function are represented as stochastic neural networks. Then, we show that we can significantly enhance the sample efficiency of GAIL leveraging the predictive density of the cost, on an extensive set of imitation learning tasks with high-dimensional states and actions.

We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category-and instance-level matching, obtaining the state-of-the-art results on the PF Pascal dataset and the InLoc indoor visual localization benchmark.

Stochastic partition models divide a multi-dimensional space into a number of rectangular regions, such that the data within each region exhibit certain types of homogeneity. Due to the nature of their partition strategy, existing partition models may create many unnecessary divisions in sparse regions when trying to describe data in dense regions. To avoid this problem we introduce a new parsimonious partition model -- the Rectangular Bounding Process (RBP) -- to efficiently partition multi-dimensional spaces, by employing a bounding strategy to enclose data points within rectangular bounding boxes. Unlike existing approaches, the RBP possesses several attractive theoretical properties that make it a powerful nonparametric partition prior on a hypercube. In particular, the RBP is self-consistent and as such can be directly extended from a finite hypercube to infinite (unbounded) space. We apply the RBP to regression trees and relational models as a flexible partition prior. The experimental results validate the merit of the RBP {in rich yet parsimonious expressiveness} compared to the state-of-the-art methods.

Pope Leo XIV called Sunday for an immediate ceasefire in the war involving Iran, marking the pontiff's strongest remarks yet on conflict.

A large class of machine learning techniques requires the solution of optimization problems involving spectral functions of parametric matrices, e.g.

Could Iran be using China's BeiDou system? Ukraine wants money and technology as payback after sending specialists to the Middle East to help down Iranian drones during the ongoing Israel-United States war with Iran . President Volodymyr Zelenskyy told reporters on Sunday that three teams were sent to the region to undertake expert assessments and demonstrate how drone defences work as countries in the Middle East continue to be targeted by Iran over hosting US military bases. We are not at war with Iran," Zelenskyy said. Earlier this week, Ukraine's leader announced military teams were sent to Qatar, the United Arab Emirates, Saudi Arabia, and a US military base in Jordan. But he explained that more long-term drone deals could be negotiated with Gulf countries, and what Kyiv gets in return for its assistance still needs to be established. "For us today, both the technology and the funding are important," Zelenskyy said. Throughout the four-year Russia-Ukraine war, Moscow has widely used Iranian Shahed-136 "suicide" drones, giving Kyiv expertise in knowing how to down the unmanned aerial vehicles through cheap drone interceptors, electronic jamming tools, and anti-aircraft weaponry. However, US President Donald Trump has said he does not need Ukraine's help in taking down Iranian drones attacking American targets. Zelenskyy said he doesn't know why Washington hasn't signed a drone agreement with Kyiv, which it has pushed for months. "I wanted to sign a deal worth about $35bn-50bn," he said. Still, as the Russia-Ukraine conflict continues with no end in sight, Zelenskyy raised concerns that the ongoing war in the Middle East will impact Kyiv's supplies of air defence missiles. "We would very much not like the United States to step away from the issue of Ukraine because of the Middle East," he told reporters. But as interest has grown for Ukrainian drone interceptors in light of the war, Zelenskyy said Kyiv's rules to buy the drones must be tightened, with foreign countries and firms being unable to bypass the government and talk directly to manufacturers. "Unfortunately, representatives of certain governments or companies want to bypass the Ukrainian state to purchase specific equipment," Zelensky told reporters. "Even in some free countries, we do not initially receive contracts from the private sector.

This paper studies the problem of sparse regression where the goal is to learn a sparse vector that best optimizes a given objective function. Under the assumption that the objective function satisfies restricted strong convexity (RSC), we analyze orthogonal matching pursuit (OMP), a greedy algorithm that is used heavily in applications, and obtain support recovery result as well as a tight generalization error bound for OMP. Furthermore, we obtain lower bounds for OMP, showing that both our results on support recovery and generalization error are tight up to logarithmic factors. To the best of our knowledge, these support recovery and generalization bounds are the first such matching upper and lower bounds (up to logarithmic factors) for {\em any} sparse regression algorithm under the RSC assumption.

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Two people have died following an outbreak of invasive meningitis at the University of Kent. BBC South East understands that a further 11 people from the Canterbury area are currently in hospital and reported to be seriously ill. It is understood that most are aged between 18 and 21 and are students at the university. Both of the people who have died are also believed to be between 18 and 21, with one also confirmed to be a student. More than 30,000 students, staff and their families are being contacted by the UK Health Security Agency (UKHSA) to inform them of the situation.

Embark Studios' CEO Patrick Söderlund admitted that there is a quality difference when it comes to using voice actors versus AI. In an unexpected twist, humans have taken some jobs back from AI. Embark Studios' CEO Patrick Söderlund recently told that the studio re-recorded some of the AI-generated voice lines in with human voices, only after its successful launch in October. There is a quality difference, Söderlund told A real professional actor is better than AI; that's just how it is. With Arc Raiders' player count peaking at nearly half a million users on Steam, the game's breakout success was still marred by its use of text-to-speech AI. While there was no generative AI used for the visuals of the extraction shooter, Embark Studios paid its actors for approval to license their voices for text-to-speech AI, according to Söderlund.