Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling

Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling… The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, na”{i}ve noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue […]

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Machine Translation of Novels in the Age of Transformer

In this chapter we build a machine translation (MT) system tailored to the literary domain, specifically to novels, based on the state-of-the-art architecture in neural MT (NMT), the Transformer (Vaswani et al., 2017), for the translation direction English-to-Catalan. Subsequently, we assess to what extent such a system can be useful by evaluating its translations, by comparing this MT system against three other systems (two domain-specific systems under the recurrent and phrase-based paradigms and a popular generic on-line system) on three […]

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Learning to Use Future Information in Simultaneous Translation

Simultaneous neural machine translation (briefly, NMT) has attracted much attention recently. In contrast to standard NMT, where the NMT system can access the full input sentence, simultaneous NMT is a prefix-to-prefix problem, where the system can only utilize the prefix of the input sentence and thus more uncertainty and difficulty are introduced to decoding… Wait-k inference is a simple yet effective strategy for simultaneous NMT, where the decoder generates the output sequence $k$ words behind the input words. For wait-k […]

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Adversarial machine learning and instrumental variables for flexible causal modeling

We are going through a new shift in machine learning (ML), where ML models are increasingly being used to automate decision-making in a multitude of domains: what personalized treatment should be administered to a patient, what discount should be offered to an online customer, and other important decisions that can greatly impact people’s lives. The machine learning revolution was primarily driven by problems that are distant from such decision-making scenarios. The first scenarios include predicting what an image depicts, predicting […]

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A Python Code to Determine Orbital Parameters of Spectroscopic Binaries

We present the open source Python code BinaryStarSolver that solves for the orbital elements of a spectroscopic binary system. Given a time-series of radial velocity measurements, six orbital parameters are determined: the long-term mean, or systemic, radial velocity, the velocity amplitude, the argument of periastron, the eccentricity, the epoch of periastron, and the orbital period referred to by ${{gamma, K, omega, e, T_0, P}}$ respectively… Also returned to the user is the projected length of the semi-major axis, $a_{1}sin(i)$, and […]

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Efficient Scene Compression for Visual-based Localization

Estimating the pose of a camera with respect to a 3D reconstruction or scene representation is a crucial step for many mixed reality and robotics applications. Given the vast amount of available data nowadays, many applications constrain storage and/or bandwidth to work efficiently… To satisfy these constraints, many applications compress a scene representation by reducing its number of 3D points. While state-of-the-art methods use $K$-cover-based algorithms to compress a scene, they are slow and hard to tune. To enhance speed […]

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Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers

Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and solves each sub-domain using faster but weaker incomplete verifiers, such as Linear Programming (LP) on linearly relaxed sub-domains… In this paper, we propose to use the backward mode linear relaxation based perturbation analysis (LiRPA) to replace LP during the BaB process, which can be efficiently implemented on the typical machine […]

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Point and Ask: Incorporating Pointing into Visual Question Answering

Visual Question Answering (VQA) has become one of the key benchmarks of visual recognition progress. Multiple VQA extensions have been explored to better simulate real-world settings: different question formulations, changing training and test distributions, conversational consistency in dialogues, and explanation-based answering… In this work, we further expand this space by considering visual questions that include a spatial point of reference. Pointing is a nearly universal gesture among humans, and real-world VQA is likely to involve a gesture towards the target […]

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Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning

Network seeding for efficient information diffusion over time-varying graphs~(TVGs) is a challenging task with many real-world applications. There are several ways to model this spatio-temporal influence maximization problem, but the ultimate goal is to determine the best moment for a node to start the diffusion process… In this context, we propose Spatio-Temporal Influence Maximization~(STIM), a model trained with Reinforcement Learning and Graph Embedding over a set of artificial TVGs that is capable of learning the temporal behavior and connectivity pattern […]

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Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints

We present gP4Pc, a new method for computing the absolute pose of a generalized camera with unknown internal scale from four corresponding 3D point-and-ray pairs. Unlike most pose-and-scale methods, gP4Pc is based on constraints arising from the congruence of shapes defined by two sets of four points related by an unknown similarity transformation… By choosing a novel parametrization for the problem, we derive a system of four quadratic equations in four scalar variables. The variables represent the distances of 3D […]

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