Deep Learning for Economists

• URL: http://arxiv.org/abs/2407.15339v3
• Date: Wed, 13 Nov 2024 15:25:32 GMT
• Title: Deep Learning for Economists
• Abstract summary: Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.
• Score: 2.44755919161855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.. The review is accompanied by a companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.

Related papers

• Data Pyramid for Embodied Manipulation [135.16063649795234]
  Embodied agents require data that couple observations with physical states and actions.<n>We organize the embodied data ecosystem as a "pyramid" spanning five complementary sources.
  arXiv  Detail & Related papers  (2026-07-27T17:59:58Z)
• Methods for Knowledge Graph Construction from Text Collections: Development and Applications [0.18275108630751835]
  The volume and variety of data across all domains has created both unprecedented opportunities and pressing challenges for extracting actionable knowledge.<n>The extraction of rich semantic knowledge demands the deployment of scalable and flexible automatic methods across text genres and schema specifications.<n>In this thesis, we experiment with the application of Natural Language Processing, Machine Learning and Generative AI methods, powered by Semantic Web best practices, to the automatic construction of Knowledge Graphs from large text corpora.
  arXiv  Detail & Related papers  (2026-03-26T19:36:00Z)
• A Survey on Generative Recommendation: Data, Model, and Tasks [55.36322811257545]
  generative recommendation reconceptualizes recommendation as a generation task rather than discriminative scoring.<n>This survey provides a comprehensive examination through a unified tripartite framework spanning data, model, and task dimensions.<n>We identify five key advantages: world knowledge integration, natural language understanding, reasoning capabilities, scaling laws, and creative generation.
  arXiv  Detail & Related papers  (2025-10-31T04:02:58Z)
• Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models [0.65268245109828]
  We introduce the notion of contextual diversity for active learning CDAL. We propose a data repair algorithm to curate contextually fair data to reduce model bias. We are working on developing image retrieval system for wildlife camera trap images and reliable warning system for poor quality rural roads.
  arXiv  Detail & Related papers  (2024-11-04T09:43:33Z)
• RESTOR: Knowledge Recovery through Machine Unlearning [71.75834077528305]
  Large language models trained on web-scale corpora can memorize undesirable datapoints. Many machine unlearning methods have been proposed that aim to 'erase' these datapoints from trained models. We propose the RESTOR framework for machine unlearning based on the following dimensions.
  arXiv  Detail & Related papers  (2024-10-31T20:54:35Z)
• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• Synthetic continued pretraining [29.6872772403251]
  We propose synthetic continued pretraining on a small corpus of domain-specific documents. We instantiate this proposal with EntiGraph, a synthetic data augmentation algorithm. We show how synthetic data augmentation can "rearrange" knowledge to enable more data-efficient learning.
  arXiv  Detail & Related papers  (2024-09-11T17:21:59Z)
• Masked Image Modeling: A Survey [73.21154550957898]
  Masked image modeling emerged as a powerful self-supervised learning technique in computer vision. We construct a taxonomy and review the most prominent papers in recent years. We aggregate the performance results of various masked image modeling methods on the most popular datasets.
  arXiv  Detail & Related papers  (2024-08-13T07:27:02Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text [3.114960935006655]
  This paper aims to improve the state of the art of automatic construction and completion of knowledge graphs from text. In this context, one emerging paradigm is in-context learning where a language model is used as it is with a prompt.
  arXiv  Detail & Related papers  (2023-05-15T17:10:19Z)
• Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation [50.46219766161111]
  We develop a self-learning model to explore the constrastive learning in the domain of misinformation identification. Our model shows the superior performance of non-matched image-text pair detection when the training data is insufficient.
  arXiv  Detail & Related papers  (2023-04-19T02:53:59Z)
• A Survey of Learning on Small Data: Generalization, Optimization, and Challenge [101.27154181792567]
  Learning on small data that approximates the generalization ability of big data is one of the ultimate purposes of AI. This survey follows the active sampling theory under a PAC framework to analyze the generalization error and label complexity of learning on small data. Multiple data applications that may benefit from efficient small data representation are surveyed.
  arXiv  Detail & Related papers  (2022-07-29T02:34:19Z)
• Efficient Document Image Classification Using Region-Based Graph Neural Network [4.147346416230273]
  Document image classification remains a popular research area because it can be commercialized in many enterprise applications across different industries. Recent advancements in large pre-trained computer vision and language models and graph neural networks has lent document image classification many tools. We propose an efficient document image classification framework that uses graph convolution neural networks.
  arXiv  Detail & Related papers  (2021-06-25T17:57:04Z)
• ENT-DESC: Entity Description Generation by Exploring Knowledge Graph [53.03778194567752]
  In practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. We introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. We propose a multi-graph structure that is able to represent the original graph information more comprehensively.
  arXiv  Detail & Related papers  (2020-04-30T14:16:19Z)
• Knowledge Guided Metric Learning for Few-Shot Text Classification [22.832467388279873]
  We propose to introduce external knowledge into few-shot learning to imitate human knowledge. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. We demonstrate that our method outperforms the state-of-the-art few-shot text classification models.
  arXiv  Detail & Related papers  (2020-04-04T10:56:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.