Search Results for author:
Found 15 papers, 5 papers with code
Low Resource Quadratic Forms for Knowledge Graph Embeddings
We address the problem of link prediction between entities and relations of knowledge graphs.
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
In this paper, we show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear.
Domain Agnostic Few-Shot Learning For Document Intelligence
In this work, we address the problem of few-shot document image classification under domain shift.
Deep learning for blocking in entity matching: a design space exploration
In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking for EM.
Ranked #5 on
Blocking
on Abt-Buy
Efficient Document Image Classification Using Region-Based Graph Neural Network
Recent advancements in large pre-trained computer vision and language models and graph neural networks has lent document image classification many tools.
Ranked #1 on
Document Image Classification
on Tobacco-3482
(Memory metric)
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection
Metric space magnitude, an active field of research in algebraic topology, is a scalar quantity that summarizes the effective number of distinct points that live in a general metric space.
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
The scalability of Nystr\"{o}mformer enables application to longer sequences with thousands of tokens.
Ranked #13 on
Semantic Textual Similarity
on MRPC
(F1 metric)
Graph Neural Networks to Predict Customer Satisfaction Following Interactions with a Corporate Call Center
Customer satisfaction is an important factor in creating and maintaining long-term relationships with customers.
A Simple yet Brisk and Efficient Active Learning Platform for Text Classification
In this work, we propose the use of a fully managed machine learning service, which utilizes active learning to directly build models from unstructured data.
SGD Distributional Dynamics of Three Layer Neural Networks
In this paper, we seek to extend the mean field results of Mei et al. (2018) from two-layer neural networks with one hidden layer to three-layer neural networks with two hidden layers.
Simplicial 2-Complex Convolutional Neural Networks
Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph.
Practical applications of metric space magnitude and weighting vectors
In a more general setting, the magnitude of a metric space is a real number that aims to quantify the effective number of distinct points in the space.
Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimation
Ordinal regression aims to classify instances into ordinal categories.
Efficient Relative Attribute Learning using Graph Neural Networks
A sizable body of work on relative attributes provides compelling evidence that relating pairs of images along a continuum of strength pertaining to a visual attribute yields significant improvements in a wide variety of tasks in vision.
Evaluating Crowdsourcing Participants in the Absence of Ground-Truth
Given a supervised/semi-supervised learning scenario where multiple annotators are available, we consider the problem of identification of adversarial or unreliable annotators.
