We extend the flow-module (introduced in Section 3.2) which aims at reusing contextual information from single-to multi-frame context. We thus consider a context(xi)i J1;cK consisting of c frames. Just as before, for a given levelrk and i in J1;cK, we use F to compute the corresponding fusion maskmki and optical flowfki between intermediate encoded featureseki (from context framexi) and the decoded featuresdks (that we wish to update). We recall that fusion masksmki handle occlusion by indicating for each spatial location the relevance of warped context features e ki = W(eki,fki), that is, whether features e kicorrespond to dks at that location in terms of content, or not.

However, current approaches can only model distributions for which training samples are directly accessible, which is not the case in many real-world tasks.

Proposition 1. Suppose that any signal The total observation loss is defined in Equation equation 4 below. After introducing some notation, we will formalize the assumptions made in the proposition. Definition 2. We define the scattering map as the (measurable) map sending signal In other words, given all possible observations of a signal, we can uniquely reconstruct the signal (for the class of signals under consideration). Observations generated by our model are slices of total observations. Thus, our model is limited to modeling the space over observations that are a member of the total observations set, i.e., The predicted distribution over signals can be recovered from the distribution over observations.

However, current approaches can only model distributions for which training samples are directly accessible, which is not the case in many real-world tasks.

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The paper presents and evaluates the power of parallel search for exact MAP inference in graphical models. We introduce a new parallel shared-memory recursive best-first AND/OR search algorithm, called SPRBFAOO, that explores the search space in a best-first manner while operating with restricted memory. Our experiments show that SPRBFAOO is often superior to the current state-of-the-art sequential AND/OR search approaches, leading to considerable speed-ups (up to 7-fold with 12 threads), especially on hard problem instances.

Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals. However, their applicability is limited to scenarios where training samples are readily available, which is not always the case in real-world applications. For example, in inverse graphics, the goal is to generate samples from a distribution of 3D scenes that align with a given image, but ground-truth 3D scenes are unavailable and only 2D images are accessible. To address this limitation, we propose a novel class of denoising diffusion probabilistic models that learn to sample from distributions of signals that are never directly observed. Instead, these signals are measured indirectly through a known differentiable forward model, which produces partial observations of the unknown signal. Our approach involves integrating the forward model directly into the denoising process. This integration effectively connects the generative modeling of observations with the generative modeling of the underlying signals, allowing for end-to-end training of a conditional generative model over signals. During inference, our approach enables sampling from the distribution of underlying signals that are consistent with a given partial observation. We demonstrate the effectiveness of our method on three challenging computer vision tasks. For instance, in the context of inverse graphics, our model enables direct sampling from the distribution of 3D scenes that align with a single 2D input image.

The use of deep pretrained bidirectional transformers has led to remarkable progress in learning multi-sentence representations for downstream language understanding tasks (Devlin et al., 2018). For tasks that make pairwise comparisons, e.g. matching a given context with a corresponding response, two approaches have permeated the literature. A Cross-encoder performs full self-attention over the pair; a Bi-encoder performs self-attention for each sequence separately, and the final representation is a function of the pair. While Cross-encoders nearly always outperform Bi-encoders on various tasks, both in our work and others' (Urbanek et al., 2019), they are orders of magnitude slower, which hampers their ability to perform real-time inference. In this work, we develop a new architecture, the Poly-encoder, that is designed to approach the performance of the Cross-encoder while maintaining reasonable computation time. Additionally, we explore two pretraining schemes with different datasets to determine how these affect the performance on our chosen dialogue tasks: ConvAI2 and DSTC7 Track 1. We show that our models achieve state-of-the-art results on both tasks; that the Poly-encoder is a suitable replacement for Bi-encoders and Cross-encoders; and that even better results can be obtained by pretraining on a large dialogue dataset.