Fetching the paper…
Reading the bibliography…
The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs).
On finding the maxima of a set of vectors
Kung, H.-T., Luccio, F., & Preparata, F. P. (1975) · 1975
Earlier work this paper cites.
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · 1998
Earlier work this paper cites.
Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach
Zitzler, E., & Thiele, L. (1999) · 1999
Earlier work this paper cites.
Multi-objective optimization using evolutionary algorithms
Deb, K. (2001) · 2001
Earlier work this paper cites.
Stochastic method for the solution of unconstrained vector optimization problems
Schäffler, S., Schultz, R., & Weinzierl, K. (2002) · 2002
Earlier work this paper cites.
Gaussian processes in machine learning
Rasmussen, C. E. (2003) · 2003
Earlier work this paper cites.
Monte carlo sampling methods
Shapiro, A. (2003) · 2003
Earlier work this paper cites.
Reference point based multi-objective optimization using evolutionary algorithms
Deb, K., & Sundar, J. (2006) · 2006
Earlier work this paper cites.
Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
Knowles, J. (2006) · 2006
Earlier work this paper cites.
The computation of the expected improvement in dominated hypervolume of pareto front approximations
Emmerich, M., & Klinkenberg, J.-w. (2008) · 2008
Earlier work this paper cites.
Multiobjective optimization on a limited budget of evaluations using model-assisted { \{ S } \} -metric selection
Ponweiser, W., Wagner, T., Biermann, D., & Vincze, M. (2008) · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images.
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
Earlier work this paper cites.
A preference-based evolutionary algorithm for multi-objective optimization
Thiele, L., Miettinen, K., Korhonen, P. J., & Molina, J. (2009) · 2009
Earlier work this paper cites.
MNIST handwritten digit database.
LeCun, Y., & Cortes, C. (2010) · 2010
Earlier work this paper cites.
Preference-based solution selection algorithm for evolutionary multiobjective optimization
Kim, J.-H., Han, J.-H., Kim, Y.-H., Choi, S.-H., & Kim, E.-S. (2011) · 2011
Earlier work this paper cites.
Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Désidéri, J.-A. (2012) · 2012
Earlier work this paper cites.
Information-theoretic regret bounds for gaussian process optimization in the bandit setting
Srinivas, N., Krause, A., Kakade, S. M., & Seeger, M. W. (2012) · 2012
Earlier work this paper cites.
Active learning of pareto fronts
Campigotto, P., Passerini, A., & Battiti, R. (2013) · 2013
Earlier work this paper cites.
A survey of multi-objective sequential decision-making
Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013) · 2013
Earlier work this paper cites.
Active learning for multi-objective optimization
Zuluaga, M., Sergent, G., Krause, A., & Püschel, M. (2013) · 2013
Earlier work this paper cites.
Deep speech: Scaling up end-to-end speech recognition.
Hannun, A., Case, C., Casper, J., Catanzaro, B., Diamos, G., Elsen, E., Prenger, R., Satheesh, S., Sengupta, S., Coates, A., et al. (2014) · 2014
Earlier work this paper cites.
Predictive entropy search for efficient global optimization of black-box functions
Hernández-Lobato, J. M., Hoffman, M. W., & Ghahramani, Z. (2014) · 2014
Earlier work this paper cites.
Seeds: a software engineer’s energy-optimization decision support framework
Manotas, I., Pollock, L., & Clause, J. (2014) · 2014
Earlier work this paper cites.
On using the hypervolume indicator to compare pareto fronts: Applications to multi-criteria optimal experimental design
Cao, Y., Smucker, B. J., & Robinson, T. J. (2015) · 2015
Earlier work this paper cites.
Predictive entropy search for bayesian optimization with unknown constraints.
Hernández-Lobato, J. M., Gelbart, M. A., Hoffman, M. W., Adams, R. P., & Ghahramani, Z. (2015) · 2015
Earlier work this paper cites.
Lenet-5, convolutional neural networks
LeCun, Y., et al. (2015) · 2015
Earlier work this paper cites.
Multiobjective optimization using gaussian process emulators via stepwise uncertainty reduction
Picheny, V. (2015) · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · 2015
Cited alongside, same era.
Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks
Chen, Y.-H., Emer, J., & Sze, V. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016
Cited alongside, same era.
Predictive entropy search for multi-objective bayesian optimization
Hernández-Lobato, D., Hernandez-Lobato, J., Shah, A., & Adams, R. (2016) · 2016
Cited alongside, same era.
Designing neural network hardware accelerators with decoupled objective evaluations
Bert: Pre-training of deep bidirectional transformers for language understanding.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · 2018
Later among the works it cites.
Dpp-net: Device-aware progressive search for pareto-optimal neural architectures
Dong, J.-D., Cheng, A.-C., Juan, D.-C., Wei, W., & Sun, M. (2018) · 2018
Later among the works it cites.
Learning to sample: Exploiting similarities across environments to learn performance models for configurable systems
Jamshidi, P., Velez, M., Kästner, C., & Siegmund, N. (2018) · 2018
Later among the works it cites.
Multi-objective autotuning of mobilenets across the full software/hardware stack
Lokhmotov, A., Chunosov, N., Vella, F., & Fursin, G. (2018) · 2018
Later among the works it cites.
Finding faster configurations using flash.
Nair, V., Yu, Z., Menzies, T., Siegmund, N., & Apel, S. (2018) · 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…
Hernández-Lobato, J. M., Gelbart, M. A., Reagen, B., Adolf, R., Hernández-Lobato, D., Whatmough, P. N., Brooks, D., Wei, G.-Y., & Adams, R. P. (2016) · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size.
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · 2016
Cited alongside, same era.
An uncertainty-aware approach to optimal configuration of stream processing systems
Jamshidi, P., & Casale, G. (2016) · 2016
Cited alongside, same era.
Gradient-based multiobjective optimization with uncertainties
Peitz, S., & Dellnitz, M. (2018) · 2016
Cited alongside, same era.
Paleo: A performance model for deep neural networks.
Qi, H., Sparks, E. R., & Talwalkar, A. (2016) · 2016
Cited alongside, same era.
Squad: 100,000+ questions for machine comprehension of text.
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · 2016
Cited alongside, same era.
Delight: Adding energy dimension to deep neural networks
Rouhani, B. D., Mirhoseini, A., & Koushanfar, F. (2016) · 2016
Cited alongside, same era.
A flexible framework for multi-objective bayesian optimization using random scalarizations.
Paria, B., Kandasamy, K., & Póczos, B. (2018) · 2018
Later among the works it cites.
Interactive multi-objective reinforcement learning in multi-armed bandits for any utility function
Roijers, D. M., Zintgraf, L. M., Libin, P., & Nowé, A. (2018) · 2018
Later among the works it cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018
Later among the works it cites.
Concolic testing for deep neural networks
Sun, Y., Wu, M., Ruan, W., Huang, X., Kwiatkowska, M., & Kroening, D. (2018) · 2018
Later among the works it cites.
Towards automated deep learning: Efficient joint neural architecture and hyperparameter search.
Zela, A., Klein, A., Falkner, S., & Hutter, F. (2018) · 2018
Later among the works it cites.
Mobile machine learning hardware at arm: A systems-on-chip (soc) perspective.
Zhu, Y., Mattina, M., & Whatmough, P. (2018) · 2018
Later among the works it cites.
Ordered preference elicitation strategies for supporting multi-objective decision making
Zintgraf, L. M., Roijers, D. M., Linders, S., Jonker, C. M., & Nowé, A. (2018) · 2018
Later among the works it cites.
Learning very large configuration spaces: What matters for linux kernel sizes.
Acher, M., Martin, H., Pereira, J., Blouin, A., Jézéquel, J.-M., Khelladi, D., Lesoil, L., & Barais, O. (2019) · 2019
Later among the works it cites.
Max-value entropy search for multi-objective bayesian optimization
Belakaria, S., Deshwal, A., & Doppa, J. R. (2019) · 2019
Later among the works it cites.
Ai enabling technologies: A survey.
Gadepally, V., Goodwin, J., Kepner, J., Reuther, A., Reynolds, H., Samsi, S., Su, J., & Martinez, D. (2019) · 2019
Later among the works it cites.
Transfer Learning for Performance Modeling of Deep Neural Network Systems
Iqbal, M. S., Kotthoff, L., & Jamshidi, P. (2019) · 2019
Later among the works it cites.
Tradeoffs in modeling performance of highly configurable software systems
Kolesnikov, S., Siegmund, N., Kästner, C., Grebhahn, A., & Apel, S. (2019) · 2019
Later among the works it cites.
https://commonvoice.mozilla.org/en/datasets
Mozilla (2019) · 2019
Later among the works it cites.
Practical design space exploration
Nardi, L., Koeplinger, D., & Olukotun, K. (2019) · 2019
Later among the works it cites.
Learning software configuration spaces: A systematic literature review.
Pereira, J. A., Martin, H., Acher, M., Jézéquel, J.-M., Botterweck, G., & Ventresque, A. (2019) · 2019
Later among the works it cites.
Survey and benchmarking of machine learning accelerators
Reuther, A., Michaleas, P., Jones, M., Gadepally, V., Samsi, S., & Kepner, J. (2019) · 2019
Later among the works it cites.
Fixynn: Efficient hardware for mobile computer vision via transfer learning.
Whatmough, P. N., Zhou, C., Hansen, P., Venkataramanaiah, S. K., Seo, J.-s., & Mattina, M. (2019) · 2019
Later among the works it cites.
Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., Jia, Y., & Keutzer, K. (2019) · 2019
Later among the works it cites.
Max-value entropy search for multi-objective bayesian optimization with constraints.
Belakaria, S., Deshwal, A., & Doppa, J. R. (2020) · 2020
Closest in time.
Cost-aware bayesian optimization.
Lee, E. H., Perrone, V., Archambeau, C., & Seeger, M. (2020) · 2020
Closest in time.