Search Results for author:
Found 18 papers, 3 papers with code
Entity Disambiguation via Fusion Entity Decoding
Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark.
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Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation
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.
Growing and Serving Large Open-domain Knowledge Graphs
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.
High-Throughput Vector Similarity Search in Knowledge Graphs
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.
Hamiltonian Adaptive Importance Sampling
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference.
Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering
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
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.
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
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.
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
In extreme classification settings, embedding-based neural network models are currently not competitive with sparse linear and tree-based methods in terms of accuracy.
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery.
Unsupervised Learning with Stein's Unbiased Risk Estimator
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.
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
In this paper we develop a novel computational sensing framework for sensing and recovering structured signals.
Learned D-AMP: Principled Neural Network based Compressive Image Recovery
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
The promise of compressive sensing (CS) has been offset by two significant challenges.
Consistent Parameter Estimation for LASSO and Approximate Message Passing
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
In this paper, we develop a new framework for sensing and recovering structured signals.
Parameterless Optimal Approximate Message Passing
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.
Asymptotic Analysis of LASSOs Solution Path with Implications for Approximate Message Passing
This paper concerns the performance of the LASSO (also knows as basis pursuit denoising) for recovering sparse signals from undersampled, randomized, noisy measurements.
