LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

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In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

We present Sudokube, a novel system that supports interactive speed querying on high-dimensional data using partially materialized data cubes. Given a storage budget, it judiciously chooses what projections to precompute and materialize during cube constru ...

To understand how daylight gives shape and life to architectural spaces, whether existing or imagined, requires quantifying its dynamism and energy. Maintaining these details presents a challenge to simulation and analysis methods that flatten data into di ...

As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often consid ...

Hybrid halide perovskites are currently one of the most studied semiconductors. However, due to poor intrinsic and extrinsic stability, further developments to commercialize devices based on hybrid halide perovskites are limited. Many different strategies ...

Database workloads have significantly evolved in the past twenty years. Traditional database systems that are mainly used to serve Online Transactional Processing (OLTP) workloads evolved into specialized database systems that are optimized for particular ...

Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up. Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...

The present invention describes an imaging system that allows visualization of a wide range of samples both in terms of morphology and in terms of material (e.g. density distribution, varying chemical composition, or anything that induces a change of optic ...

Grading student SQL queries manually is a tedious and error-prone process. Earlier work on testing correctness of student SQL queries, such as the XData system, can be used to test the correctness of a student query. However, in case a student query is fou ...

Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, i ...

Modern server hardware is increasingly heterogeneous as hardware accelerators, such as GPUs, are used together with multicore CPUs to meet the computational demands of modern data analytics workloads. Unfortunately, query parallelization techniques used by ...

Measuring the thermoelectric transport properties of a material is a prerequisite to determining its usefulness for application in waste heat recovery or cooling and the basis for devising improvement strategies. While well-established characterization met ...

This study proposes a comprehensive approach to investigate water resource contamination by pesticides under the specific climatic and hydrological conditions of the Sudano-Sahelian climate. Samples were collected from tradi- tional wells, boreholes, and a ...

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although ...

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples ...

While the extant innovation literature has provided extensive evidence of the so-called "demand-pull" effect, the possible diverse impact of demand evolution on product vs process innovation activities has not been yet investigated. This paper develops a f ...

This paper deals with the resolution of inverse problems in a periodic setting or, in other terms, the reconstruction of periodic continuous-domain signals from their noisy measurements. We focus on two reconstruction paradigms: variational and statistical ...

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Chattez avec Graph Search

Gibbs sampling the posterior of neural networks

Center-aware Adversarial Augmentation for Single Domain Generalization

Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

Traversing Time Dependent Light Fields for Daylight Glare Evaluation

Sampling-Based AQP in Modern Analytical Engines

Structure-property relationships and mixed conductivity in layered hybrid perovskites based on phenyl-derived spacers

Micro-architectural Analysis of Database Workloads

Timely and cost-efficient data exploration through adaptive tuning

System and methods for differential imaging using a lock-in camera

Edit Based Grading of SQL Queries

Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Development of microdevices for the in-plane thermoelectric characterization of deposited films

Assessment of water resource contamination by pesticides in vegetable-producing areas in Burkina Faso

Mirrored Langevin Dynamics

Mask Combination of Multi-Layer Graphs for Global Structure Inference

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

The role of demand in fostering product vs process innovation: a model and an empirical test

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples