Author
Michael S. Bernstein
Other affiliations: Association for Computing Machinery, Massachusetts Institute of Technology
Bio: Michael S. Bernstein is an academic researcher from Stanford University. The author has contributed to research in topics: Crowdsourcing & Computer science. The author has an hindex of 52, co-authored 191 publications receiving 42744 citations. Previous affiliations of Michael S. Bernstein include Association for Computing Machinery & Massachusetts Institute of Technology.
Papers published on a yearly basis
Papers
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Carnegie Mellon University1, University of Moratuwa2, Massachusetts Institute of Technology3, Stanford University4, University of Houston5, Boston University6, University of Brasília7, Ryerson University8, University of Washington9, University of Texas at Austin10, Maharaja Agrasen Institute of Technology11, University of Salford12
TL;DR: In this paper, the authors draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other's quality through double-blind peer assessment.
Abstract: Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality work. Reputation is one central example: crowdsourcing systems depend on reputation scores from decentralized workers and requesters, but these scores are notoriously inflated and uninformative. In this paper, we draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other's quality through double-blind peer assessment. A two-week field experiment compared crowd guilds to a traditional decentralized crowd work model. Crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than signals available on current crowd working platforms, and more accurate than in the traditional model.
52 citations
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Carnegie Mellon University1, Northwestern University2, National Autonomous University of Mexico3, LNM Institute of Information Technology4, Freelancer.com5, Indraprastha Institute of Information Technology6, Stanford University7, University of Moratuwa8, University of Reading9, University of Colorado Boulder10, University of California, Santa Cruz11, JSSATE Noida12, Hewlett-Packard13, University of Western Ontario14, University of Texas at Austin15, VIT University16, Fidelity Investments17, Sardar Vallabhbhai National Institute of Technology, Surat18, Ryerson University19, Middlesex University20, Harvard University21, Notre Dame High School22, University of Mumbai23, Stevens Institute of Technology24, University of Texas at Dallas25, New York University26, University of Osnabrück27, Intuit28, University of Copenhagen29, Thapar University30
TL;DR: This paper proposes a prototype task to improve the work quality and open-governance model to achieve equitable representation and envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.
Abstract: Crowdsourcing marketplaces provide opportunities for autonomous and collaborative professional work as well as social engagement. However, in these marketplaces, workers feel disrespected due to unreasonable rejections and low payments, whereas requesters do not trust the results they receive. The lack of trust and uneven distribution of power among workers and requesters have raised serious concerns about sustainability of these marketplaces. To address the challenges of trust and power, this paper introduces Daemo, a self-governed crowdsourcing marketplace. We propose a prototype task to improve the work quality and open-governance model to achieve equitable representation. We envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.
50 citations
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Massachusetts Institute of Technology1, Stanford University2, University of Moratuwa3, Carnegie Mellon University4, Sri Lanka Institute of Information Technology5, Maharaja Agrasen Institute of Technology6, University of Washington7, Lancaster University8, University of Houston9, Jamia Millia Islamia10, École Polytechnique Fédérale de Lausanne11, Juniper Networks12, University of California, Berkeley13, University of Mumbai14, University of Salford15, Helwan University16, University of California, San Diego17, Ryerson University18, Manipal University19
TL;DR: Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty.
Abstract: Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find, stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing platforms that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highly-rated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty.
50 citations
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04 Apr 2009TL;DR: The results of the study demonstrate the need for a tool such as the authors' to support the rapid capture and retrieval of short notes-to-self, and afford insights into how users' actual note-keeping tendencies could be used to better support their needs in future PIM tools.
Abstract: This paper describes a longitudinal field experiment in personal note-taking that examines how people capture and use information in short textual notes. Study participants used our tool, a simple browser-based textual note-taking utility, to capture personal information over the course of ten days. We examined the information they kept in notes using the tool, how this information was expressed, and aspects of note creation, editing, deletion, and search. We found that notes were recorded extremely quickly and tersely, combined information of multiple types, and were rarely revised or deleted. The results of the study demonstrate the need for a tool such as ours to support the rapid capture and retrieval of short notes-to-self, and afford insights into how users' actual note-keeping tendencies could be used to better support their needs in future PIM tools.
50 citations
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23 Feb 2013TL;DR: EmailValet is an email client that recruits remote assistants from an expert crowdsourcing marketplace that aims for parsimony and transparency in access con-trol for the crowd, and is an example of a valet approach to crowdsourcing.
Abstract: This paper introduces privacy and accountability techniques for crowd-powered systems. We focus on email task management: tasks are an implicit part of every inbox, but the overwhelming volume of incoming email can bury important requests. We present EmailValet, an email client that recruits remote assistants from an expert crowdsourcing marketplace. By annotating each email with its implicit tasks, EmailValet's assistants create a task list that is automatically populated from emails in the user's inbox. The system is an example of a valet approach to crowdsourcing, which aims for parsimony and transparency in access con-trol for the crowd. To maintain privacy, users specify rules that define a sliding-window subset of their inbox that they are willing to share with assistants. To support accountability, EmailValet displays the actions that the assistant has taken on each email. In a weeklong field study, participants completed twice as many of their email-based tasks when they had access to crowdsourced assistants, and they became increasingly comfortable sharing their inbox with assistants over time.
49 citations
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27 Jun 2016TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
123,388 citations
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04 Sep 2014TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
55,235 citations
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01 Jan 2015TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
49,914 citations
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TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.
The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
44,703 citations
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TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
38,208 citations