Our goal is to predict future video frames given a sequence of input frames. Despite large amounts of video data, this remains a challenging task because of the high-dimensionality of video frames. We address this challenge by proposing the Decompositional Disentangled Predictive Auto-Encoder (DDPAE), a framework that combines structured probabilistic models and deep networks to automatically (i) decompose the high-dimensional video that we aim to predict into components, and (ii) disentangle each component to have low-dimensional temporal dynamics that are easier to predict. Crucially, with an appropriately specified generative model of video frames, our DDPAE is able to learn both the latent decomposition and disentanglement without explicit supervision. For the Moving MNIST dataset, we show that DDPAE is able to recover the underlying components (individual digits) and disentanglement (appearance and location) as we would intuitively do. We further demonstrate that DDPAE can be applied to the Bouncing Balls dataset involving complex interactions between multiple objects to predict the video frame directly from the pixels and recover physical states without explicit supervision.

Various 3D semantic attributes such as segmentation masks, geometric features, keypoints, and materials can be encoded as per-point probe functions on 3D geometries. Given a collection of related 3D shapes, we consider how to jointly analyze such probe functions over different shapes, and how to discover common latent structures using a neural network -- even in the absence of any correspondence information. Our network is trained on point cloud representations of shape geometry and associated semantic functions on that point cloud. These functions express a shared semantic understanding of the shapes but are not coordinated in any way. For example, in a segmentation task, the functions can be indicator functions of arbitrary sets of shape parts, with the particular combination involved not known to the network. Our network is able to produce a small dictionary of basis functions for each shape, a dictionary whose span includes the semantic functions provided for that shape. Even though our shapes have independent discretizations and no functional correspondences are provided, the network is able to generate latent bases, in a consistent order, that reflect the shared semantic structure among the shapes. We demonstrate the effectiveness of our technique in various segmentation and keypoint selection applications.

Could Iran be using China's BeiDou system? Iraq's Iranian-backed Kataib Hezbollah has released drone video from an attack on the US's Victory Base near Baghdad International Airport. It's believed to be the first time the group has successfully used the FPV attack drone to skirt US defences. Iran's Space Research Centre severely damaged in strikes Thousands in Madrid protest'forgotten' Gaza, warn Iran war may spiral into

Voice cloning is a highly desired feature for personalized speech interfaces. We introduce a neural voice cloning system that learns to synthesize a person's voice from only a few audio samples. We study two approaches: speaker adaptation and speaker encoding. Speaker adaptation is based on fine-tuning a multi-speaker generative model. Speaker encoding is based on training a separate model to directly infer a new speaker embedding, which will be applied to a multi-speaker generative model. In terms of naturalness of the speech and similarity to the original speaker, both approaches can achieve good performance, even with a few cloning audios. While speaker adaptation can achieve slightly better naturalness and similarity, cloning time and required memory for the speaker encoding approach are significantly less, making it more favorable for low-resource deployment.

Iranian Crown Prince Reza Pahlavi said he is ready to lead Iran's transition as soon as the Islamic Republic falls, announcing plans for a transitional system under his leadership.

We investigate the problem of learning a Lipschitz function from binary feedback. In this problem, a learner is trying to learn a Lipschitz function $f:[0,1]^d \rightarrow [0,1]$ over the course of $T$ rounds. On round $t$, an adversary provides the learner with an input $x_t$, the learner submits a guess $y_t$ for $f(x_t)$, and learns whether $y_t > f(x_t)$ or $y_t \leq f(x_t)$. The learner's goal is to minimize their total loss $\sum_t\ell(f(x_t), y_t)$ (for some loss function $\ell$). The problem is motivated by \textit{contextual dynamic pricing}, where a firm must sell a stream of differentiated products to a collection of buyers with non-linear valuations for the items and observes only whether the item was sold or not at the posted price.

We suggest a general oracle-based framework that captures parallel stochastic optimization in different parallelization settings described by a dependency graph, and derive generic lower bounds in terms of this graph. We then use the framework and derive lower bounds to study several specific parallel optimization settings, including delayed updates and parallel processing with intermittent communication. We highlight gaps between lower and upper bounds on the oracle complexity, and cases where the ``natural'' algorithms are not known to be optimal.

Recent work has developed methods for learning deep network classifiers that are \emph{provably} robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks. In this paper, in an effort to scale these approaches to substantially larger models, we extend previous work in three main directly. First, we present a technique for extending these training procedures to much more general networks, with skip connections (such as ResNets) and general nonlinearities; the approach is fully modular, and can be implemented automatically analogously to automatic differentiation. Second, in the specific case of $\ell_\infty$ adversarial perturbations and networks with ReLU nonlinearities, we adopt a nonlinear random projection for training, which scales \emph{linearly} in the number of hidden units (previous approached scaled quadratically). Third, we show how to further improve robust error through cascade models. On both MNIST and CIFAR data sets, we train classifiers that improve substantially on the state of the art in provable robust adversarial error bounds: from 5.8% to 3.1% on MNIST (with $\ell_\infty$ perturbations of $\epsilon=0.1$),

Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and rapid inferences, but are typically associated with biases that are hard to quantify. The latter enjoy asymptotic consistency, but can suffer from high computational costs. In this paper we present a way of bridging the gap between deterministic and stochastic inference. Specifically, we suggest an efficient sequential Monte Carlo (SMC) algorithm for PGMs which can leverage the output from deterministic inference methods. While generally applicable, we show explicitly how this can be done with loopy belief propagation, expectation propagation, and Laplace approximations. The resulting algorithm can be viewed as a post-correction of the biases associated with these methods and, indeed, numerical results show clear improvements over the baseline deterministic methods as well as over plain SMC.

LA school hid student's gender switch from parents before teen's suicide, lawsuit claims I looked like a monster after a car accident burned off my face... but a pioneering face transplant gave me my life back. America's heartland to see huge population plunge by 2050 - professor has a controversial visa plan to fix it Insufferable blowhard Stephen Colbert is being taken out like the trash... and thank God! What he's done is so diabolical: MAUREEN CALLAHAN JFK Jr's mortifying night of phone sex... day Sarah Jessica Parker ditched her underwear to seduce him in public... and the girlfriend he REALLY wanted to marry: All the women before Carolyn Truth about'super secretive' Michael B. Jordan's love life... and real reason he is perpetually single: Years of private'heartache' and'loneliness' laid bare I'm raising my two-year-old on a cruise ship These are the harsh realities of life at sea Extramarital sex with witches, cursed bloodlines and possessed politicians: DC's chief exorcist reveals the potent stench of evil among America's elite I ignored my itchy legs and cold-like symptoms. Then doctors discovered something horrifying on a scan... I'm terrified I'm going to die I made a 34-page dress code for my wedding guests... critics say I'm controlling but I want it to be perfect Trump's religious inner circle implodes as beauty queen's firing sparks revolt... and'spiritual adviser' faces shocking Israel claims China's sinister'Trojan horse' that has already breached America's gates and scooped up YOUR data We fled Trump to chase the REAL American dream in the most idyllic European hotspot... here's why we're coming back to a red state Harry and Meghan explode at claim the Queen accused Markle of'brainwashing' Iran's deadly drone arsenal is a'wake-up call for America': Expert warns US defenses may be unprepared for swarm attacks A US military drone expert has warned that Iranian attack drones could potentially slip through America's defenses and strike targets on US soil. Brett Velicovich, a former US Army intelligence and special operations soldier who spent years using drones to hunt ISIS leaders before founding drone company PowerUs, said the threat comes from a new type of warfare that the US is still struggling to defend against. 'These new asymmetric threats, where you've got low-cost, cheap, small drones, in some cases, that are able to be sent in massive waves, don't have the same signature of an intercontinental ballistic missile,' Velicovich explained.