no code implementations • 2 Apr 2024 • Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander M. Rush, Umar Farooq Minhas, Yunyao Li
Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark.
Ranked #1 on Entity Linking on KORE50
no code implementations • 20 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.
no code implementations • 16 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.
no code implementations • 4 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.
no code implementations • 27 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.
no code implementations • 26 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.
no code implementations • 27 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.
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.
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.
no code implementations • ICLR 2019 • Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk
In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery.
1 code implementation • 26 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.
no code implementations • 11 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.
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.
1 code implementation • 14 Jan 2017 • Ali Mousavi, Richard G. Baraniuk
The promise of compressive sensing (CS) has been offset by two significant challenges.
no code implementations • 3 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 $\lambda$?
no code implementations • 17 Aug 2015 • Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk
In this paper, we develop a new framework for sensing and recovering structured signals.
no code implementations • 31 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.
no code implementations • 23 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.