Search Results for author: Ali Mousavi

Found 17 papers, 3 papers with code

Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation

no code implementations20 Sep 2023 Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theo Rekatsinas, Benjamin Han, Yunyao Li, Jeff Pound, Josh Susskind, Natalie Schluter, Ihab Ilyas, Navdeep Jaitly

Guided by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation.

Hallucination Knowledge Graphs

Growing and Serving Large Open-domain Knowledge Graphs

no code implementations16 May 2023 Ihab F. Ilyas, JP Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, Chiraag Sumanth

We then describe how our platform, including graph embeddings, can be leveraged to create a Semantic Annotation service that links unstructured Web documents to entities in our KG.

Entity Linking Fact Verification +2

High-Throughput Vector Similarity Search in Knowledge Graphs

no code implementations4 Apr 2023 Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi, Ihab F. Ilyas, Umar Farooq Minhas, Jeffrey Pound, Theodoros Rekatsinas

Motivated by the tasks of finding related KG queries and entities for past KG query workloads, we focus on hybrid vector similarity search (hybrid queries for short) where part of the query corresponds to vector similarity search and part of the query corresponds to predicates over relational attributes associated with the underlying data vectors.

Knowledge Graphs Vocal Bursts Intensity Prediction

Hamiltonian Adaptive Importance Sampling

no code implementations27 Sep 2022 Ali Mousavi, Reza Monsefi, Víctor Elvira

Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference.

Bayesian Inference

Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering

no code implementations26 Jan 2021 Ali Mousavi, Mehrdad Jalali, Mahdi Yaghoubi

The principle advantage and shortcoming of QC is analyzed and based on its shortcomings, an improved algorithm through a subtractive clustering method is proposed.


Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders

no code implementations27 Jul 2020 Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi

We study an OPE problem in an infinite-horizon, ergodic Markov decision process with unobserved confounders, where states and actions can act as proxies for the unobserved confounders.

Off-policy evaluation reinforcement-learning

Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning

no code implementations ICLR 2020 Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou

Off-policy estimation for long-horizon problems is important in many real-life applications such as healthcare and robotics, where high-fidelity simulators may not be available and on-policy evaluation is expensive or impossible.

reinforcement-learning Reinforcement Learning (RL)

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces

no code implementations NeurIPS 2019 Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N. Holtmann-Rice, Satyen Kale, Sashank Reddi, Sanjiv Kumar

In extreme classification settings, embedding-based neural network models are currently not competitive with sparse linear and tree-based methods in terms of accuracy.

Attribute Classification +2

Unsupervised Learning with Stein's Unbiased Risk Estimator

1 code implementation26 May 2018 Christopher A. Metzler, Ali Mousavi, Reinhard Heckel, Richard G. Baraniuk

We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems without any ground truth data.

Astronomy Image Denoising

DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks

no code implementations11 Jul 2017 Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

In this paper we develop a novel computational sensing framework for sensing and recovering structured signals.

Compressive Sensing

Learned D-AMP: Principled Neural Network based Compressive Image Recovery

1 code implementation NeurIPS 2017 Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk

The LDAMP network is easy to train, can be applied to a variety of different measurement matrices, and comes with a state-evolution heuristic that accurately predicts its performance.


Learning to Invert: Signal Recovery via Deep Convolutional Networks

1 code implementation14 Jan 2017 Ali Mousavi, Richard G. Baraniuk

The promise of compressive sensing (CS) has been offset by two significant challenges.

Compressive Sensing

Consistent Parameter Estimation for LASSO and Approximate Message Passing

no code implementations3 Nov 2015 Ali Mousavi, Arian Maleki, Richard G. Baraniuk

For instance the following basic questions have not yet been studied in the literature: (i) How does the size of the active set $\|\hat{\beta}^\lambda\|_0/p$ behave as a function of $\lambda$?

A Deep Learning Approach to Structured Signal Recovery

no code implementations17 Aug 2015 Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk

In this paper, we develop a new framework for sensing and recovering structured signals.

Compressive Sensing Denoising

Parameterless Optimal Approximate Message Passing

no code implementations31 Oct 2013 Ali Mousavi, Arian Maleki, Richard G. Baraniuk

In particular, both the final reconstruction error and the convergence rate of the algorithm crucially rely on how the threshold parameter is set at each step of the algorithm.

Compressive Sensing

Asymptotic Analysis of LASSOs Solution Path with Implications for Approximate Message Passing

no code implementations23 Sep 2013 Ali Mousavi, Arian Maleki, Richard G. Baraniuk

This paper concerns the performance of the LASSO (also knows as basis pursuit denoising) for recovering sparse signals from undersampled, randomized, noisy measurements.


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