Fetching the paper…
Reading the bibliography…
Automation of tasks can have critical consequences when humans lose agency over decision processes.
Active learning with statistical models
D. A. Cohn, Z. Ghahramani, and M. I. Jordan · 1996
Earlier work this paper cites.
Collaborative systems (aaai-94 presidential address)
B. J. Grosz · 1996
Earlier work this paper cites.
Using neural networks for data mining
M. W. Craven and J. W. Shavlik · 1997
Earlier work this paper cites.
A comparative study on feature selection in text categorization
Y. Yang and J. O. Pedersen · 1997
Earlier work this paper cites.
Principles of mixed-initiative user interfaces
E. Horvitz · 1999
Earlier work this paper cites.
The decomposition of human-written summary sentences
H. Jing and K. R. McKeown · 1999
Earlier work this paper cites.
A writer’s collaborative assistant
T. Babaian, B. J. Grosz, and S. M. Shieber · 2002
Earlier work this paper cites.
A review of explanation methods for bayesian networks
C. Lacave and F. J. Díez · 2002
Earlier work this paper cites.
Interactive machine learning
J. A. Fails and D. R. Olsen Jr · 2003
Earlier work this paper cites.
Opening the black box-data driven visualization of neural networks
F.-Y. Tzeng and K.-L. Ma · 2005
Earlier work this paper cites.
Visual analytics: Definition, process, and challenges
D. Keim, G. Andrienko, J.-D. Fekete, C. Görg, J. Kohlhammer, and G. Melançon · 2008
Earlier work this paper cites.
Why and why not explanations improve the intelligibility of context-aware intelligent systems
B. Y. Lim, A. K. Dey, and D. Avrahami · 2009
Earlier work this paper cites.
Interacting meaningfully with machine learning systems: Three experiments
S. Stumpf, V. Rajaram, L. Li, W.-K. Wong, M. Burnett, T. Dietterich, E. Sullivan, and J. Herlocker · 2009
Earlier work this paper cites.
Soylent: a word processor with a crowd inside
M. S. Bernstein, G. Little, R. C. Miller, B. Hartmann, M. S. Ackerman, D. R. Karger, D. Crowell, and K. Panovich · 2010
Earlier work this paper cites.
Explanatory debugging: Supporting end-user debugging of machine-learned programs
T. Kulesza, S. Stumpf, M. Burnett, W.-K. Wong, Y. Riche, T. Moore, I. Oberst, A. Shinsel, and K. McIntosh · 2010
Earlier work this paper cites.
Close engagements with artificial companion, 2010
Y. Wilks · 2010
Earlier work this paper cites.
TasteWeights: a visual interactive hybrid recommender system
S. Bostandjiev, J. O’Donovan, and T. Höllerer · 2012
Earlier work this paper cites.
An affordance-based framework for human computation and human-computer collaboration
R. J. Crouser and R. Chang · 2012
Earlier work this paper cites.
Semantic interaction for visual text analytics
A. Endert, P. Fiaux, and C. North · 2012
Earlier work this paper cites.
Making machine learning models interpretable
A. Vellido, J. D. Martín-Guerrero, and P. J. Lisboa · 2012
Earlier work this paper cites.
Power to the people: The role of humans in interactive machine learning
S. Amershi, M. Cakmak, W. B. Knox, and T. Kulesza · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
Earlier work this paper cites.
Human effort and machine learnability in computer aided translation
S. Green, S. I. Wang, J. Chuang, J. Heer, S. Schuster, and C. D. Manning · 2014
Earlier work this paper cites.
Progressive visual analytics: User-driven visual exploration of in-progress analytics
C. D. Stolper, A. Perer, and D. Gotz · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad · 2015
Earlier work this paper cites.
Predicting multiple structured visual interpretations
D. Dey, V. Ramakrishna, M. Hebert, and J. Andrew Bagnell · 2015
Earlier work this paper cites.
Principles of explanatory debugging to personalize interactive machine learning
T. Kulesza, M. Burnett, W.-K. Wong, and S. Stumpf · 2015
Earlier work this paper cites.
Visualizing and understanding neural models in nlp
J. Li, X. Chen, E. Hovy, and D. Jurafsky · 2015
Earlier work this paper cites.
Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. C. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio · 2015
Earlier work this paper cites.
Understanding neural networks through deep visualization
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson · 2015
Cited alongside, same era.
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
A. Holzinger · 2016
Cited alongside, same era.
Globally coherent text generation with neural checklist models
C. Kiddon, L. Zettlemoyer, and Y. Choi · 2016
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability
B. Kim, R. Khanna, and O. O. Koyejo · 2016
Cited alongside, same era.
Interacting with predictions: Visual inspection of black-box machine learning models
J. Krause, A. Perer, and K. Ng · 2016
Cited alongside, same era.
Hierarchical neural story generation
A. Fan, M. Lewis, and Y. Dauphin · 2018
Later among the works it cites.
Bottom-up abstractive summarization
S. Gehrmann, Y. Deng, and A. Rush · 2018
Later among the works it cites.
Visual analytics in deep learning: An interrogative survey for the next frontiers
F. M. Hohman, M. Kahng, R. Pienta, and D. H. Chau · 2018
Later among the works it cites.
Improving fairness in machine learning systems: What do industry practitioners need?
K. Holstein, J. W. Vaughan, H. Daumé III, M. Dudík, and H. Wallach · 2018
Later among the works it cites.
Semi-supervised prediction-constrained topic models
M. C. Hughes, G. Hope, L. Weiner, T. H. McCoy Jr, R. H. Perlis, E. B. Sudderth, and F. Doshi-Velez · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Lei, R. Barzilay, and T. Jaakkola · 2016
Cited alongside, same era.
The mythos of model interpretability
Z. C. Lipton · 2016
Cited alongside, same era.
Deconvolution and checkerboard artifacts
A. Odena, V. Dumoulin, and C. Olah · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Cited alongside, same era.
Embedding projector: Interactive visualization and interpretation of embeddings
D. Smilkov, N. Thorat, C. Nicholson, E. Reif, F. B. Viégas, and M. Wattenberg · 2016
Cited alongside, same era.
What do neural machine translation models learn about morphology?
Y. Belinkov, N. Durrani, F. Dalvi, H. Sajjad, and J. Glass · 2017
Cited alongside, same era.
Rnnbow: Visualizing learning via backpropagation gradients in recurrent neural networks
D. Cashman, G. Patterson, A. Mosca, and R. Chang · 2017
Cited alongside, same era.
Activis: Visual exploration of industry-scale deep neural network models
M. Kahng, P. Y. Andrews, A. Kalro, and D. H. P. Chau · 2018
Later among the works it cites.
A tutorial on deep latent variable models of natural language
Y. Kim, S. Wiseman, and A. M. Rush · 2018
Later among the works it cites.
V. Lai and C. Tan · 2018
Later among the works it cites.
Deepeyes: Progressive visual analytics for designing deep neural networks
N. Pezzotti, T. Höllt, J. Van Gemert, B. P. Lelieveldt, E. Eisemann, and A. Vilanova · 2018
Later among the works it cites.
Debugging sequence-to-sequence models with Seq2Seq-Vis
H. Strobelt, S. Gehrmann, M. Behrisch, A. Perer, H. Pfister, and A. Rush · 2018
Later among the works it cites.
LSTMVis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
H. Strobelt, S. Gehrmann, H. Pfister, and A. M. Rush · 2018
Later among the works it cites.
Ganviz: A visual analytics approach to understand the adversarial game
J. Wang, L. Gou, H. Yang, and H.-W. Shen · 2018
Later among the works it cites.
High-resolution image synthesis and semantic manipulation with conditional gans
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro · 2018
Later among the works it cites.
Toward ethical natural language generation for human-robot interaction
T. Williams · 2018
Later among the works it cites.
Visualizing dataflow graphs of deep learning models in tensorflow
K. Wongsuphasawat, D. Smilkov, J. Wexler, J. Wilson, D. Mané, D. Fritz, D. Krishnan, F. B. Viégas, and M. Wattenberg · 2018
Later among the works it cites.
Machine learning as a ux design material: How can we imagine beyond automation, recommenders, and reminders?
Q. Yang · 2018
Later among the works it cites.
Guidelines for human-ai interaction
S. Amershi, D. Weld, M. Vorvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. Iqbal, P. N. Bennett, K. Inkpen, et al · 2019
Closest in time.
Analysis methods in neural language processing: A survey
Y. Belinkov and J. Glass · 2019
Closest in time.
Activation atlas
S. Carter, Z. Armstrong, L. Schubert, I. Johnson, and C. Olah · 2019
Closest in time.
What is one grain of sand in the desert? analyzing individual neurons in deep nlp models
F. Dalvi, N. Durrani, H. Sajjad, Y. Belinkov, A. Bau, and J. Glass · 2019
Closest in time.
Agency plus automation: Designing artificial intelligence into interactive systems
J. Heer · 2019
Closest in time.
Music transformer: Generating music with long-term structure
C.-Z. A. Huang, A. Vaswani, J. Uszkoreit, N. Shazeer, I. Simon, C. Hawthorne, A. Dai, M. Hoffman, M. Dinculescu, and D. Eck · 2019
Closest in time.
Recent research advances on interactive machine learning
L. Jiang, S. Liu, and C. Chen · 2019
Closest in time.
RetainVis: Visual analytics with interpretable and interactive recurrent neural networks on electronic medical records
B. C. Kwon, M.-J. Choi, J. T. Kim, E. Choi, Y. B. Kim, S. Kwon, J. Sun, and J. Choo · 2019
Closest in time.
NLIZE: A perturbation-driven visual interrogation tool for analyzing and interpreting natural language inference models
S. Liu, Z. Li, T. Li, V. Srikumar, V. Pascucci, and P.-T. Bremer · 2019
Closest in time.
Semantic image synthesis with spatially-adaptive normalization
T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu · 2019
Closest in time.
Vis4ml: An ontology for visual analytics assisted machine learning
D. Sacha, M. Kraus, D. A. Keim, and M. Chen · 2019
Closest in time.
Seq2Seq-Vis: A visual debugging tool for sequence-to-sequence models
H. Strobelt, S. Gehrmann, M. Behrisch, A. Perer, H. Pfister, and A. M. Rush · 2019
Closest in time.
Understanding the effect of accuracy on trust in machine learning models
M. Yin, J. Wortman Vaughan, and H. Wallach · 2019
Closest in time.
Manifold: A model-agnostic framework for interpretation and diagnosis of machine learning models
J. Zhang, Y. Wang, P. Molino, L. Li, and D. S. Ebert · 2019
Closest in time.