| 1. |
- Håkansson, Maria, et al.
(författare)
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Facilitating Mobile Music Sharing and Social Interaction with Push!Music
- 2007
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Ingår i: Proceedings of the 40th Hawaii International Conference on System Sciences. - Los Alamitos, Calif. : IEEE Computer Society Washington. - 1530-1605. - 0769527558 ; , s. 87-
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Konferensbidrag (refereegranskat)abstract
- Push!Music is a novel mobile music listening and sharing system, where users automatically receive songs that have autonomously recommended themselves from nearby players depending on similar listening behaviour and music history. Push!Music also enables users to wirelessly send songs between each other as personal recommendations. We conducted a two-week preliminary user study of Push!Music, where a group of five friends used the application in their everyday life. We learned for example that the shared music in Push!Music became a start for social interaction and that received songs in general were highly appreciated and could be looked upon as 'treats'.
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| 2. |
- Linnusson, Henrik, et al.
(författare)
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Efficient conformal predictor ensembles
- 2020
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 397, s. 266-278
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study a generalization of a recently developed strategy for generating conformal predictor ensembles: out-of-bag calibration. The ensemble strategy is evaluated, both theoretically and empirically, against a commonly used alternative ensemble strategy, bootstrap conformal prediction, as well as common non-ensemble strategies. A thorough analysis is provided of out-of-bag calibration, with respect to theoretical validity, empirical validity (error rate), efficiency (prediction region size) and p-value stability (the degree of variance observed over multiple predictions for the same object). Empirical results show that out-of-bag calibration displays favorable characteristics with regard to these criteria, and we propose that out-of-bag calibration be adopted as a standard method for constructing conformal predictor ensembles.
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| 3. |
- Morger, Andrea, et al.
(författare)
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Assessing the calibration in toxicological in vitro models with conformal prediction
- 2021
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Ingår i: Journal of Cheminformatics. - : BioMed Central. - 1758-2946. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the query samples have drifted from the training data's descriptor space. Thus, the assumption for applying machine learning algorithms, that training and test data stem from the same distribution, might not always be fulfilled. In this work, conformal prediction is used to assess the calibration of the models. Deviations from the expected error may indicate that training and test data originate from different distributions. Exemplified on the Tox21 datasets, composed of chronologically released Tox21Train, Tox21Test and Tox21Score subsets, we observed that while internally valid models could be trained using cross-validation on Tox21Train, predictions on the external Tox21Score data resulted in higher error rates than expected. To improve the prediction on the external sets, a strategy exchanging the calibration set with more recent data, such as Tox21Test, has successfully been introduced. We conclude that conformal prediction can be used to diagnose data drifts and other issues related to model calibration. The proposed improvement strategy-exchanging the calibration data only-is convenient as it does not require retraining of the underlying model.
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| 4. |
- Norinder, Ulf, 1956-, et al.
(författare)
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Conformal prediction to define applicability domain : A case study on predicting ER and AR binding
- 2016
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis. - 1062-936X .- 1029-046X. ; 27:4, s. 303-316
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Tidskriftsartikel (refereegranskat)abstract
- A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.
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| 5. |
- Stathis, Dimitrios, et al.
(författare)
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eBrainII : a 3 kW Realtime Custom 3D DRAM Integrated ASIC Implementation of a Biologically Plausible Model of a Human Scale Cortex
- 2020
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Ingår i: Journal of Signal Processing Systems. - : Springer. - 1939-8018 .- 1939-8115. ; 92:11, s. 1323-1343
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Tidskriftsartikel (refereegranskat)abstract
- The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not biologically plausible. Despite their initial success, they cannot attain the cognitive capabilities enabled by the dynamic hierarchical associative memory systems of biological brains. The biologically plausible spiking brain models, e.g., cortex, basal ganglia, and amygdala, have a greater potential to achieve biological brain like cognitive capabilities. Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically plausible spiking model of the cortex. A human-scale model of BCPNN in real-time requires 162 TFlop/s, 50 TBs of synaptic weight storage to be accessed with a bandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s. A hand-optimized implementation of rodent scale BCPNN has been done on Tesla K80 GPUs require 3 kWs, we extrapolate from that a human scale network will require 3 MWs. These power numbers rule out such implementations for field deployment as cognition engines in embedded systems. The key innovation that this paper reports is that it is feasible and affordable to implement real-time BCPNN as a custom tiled application-specific integrated circuit (ASIC) in 28 nm technology with custom 3D DRAM - eBrainII - that consumes 3 kW for human scale and 12 watts for rodent scale. Such implementations eminently fulfill the demands for field deployment.
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| 6. |
- Sutinen, Martti, et al.
(författare)
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Web-Based Analytical Decision Support System
- 2010
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Ingår i: Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. - : IEEE conference proceedings. - 9781424481347 - 9781424481354 ; , s. 575-579
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Konferensbidrag (refereegranskat)abstract
- This paper presents a web-application supporting structured decision modelling and analysis. The application allows for decision modelling with respect to different preferences and views, allowing for numerically imprecise and vague background probabilities, values, and criteria weights, which further can be adjusted in an interactive fashion when considering calculated decision outcomes. The web-application is based on a decision tool that has been used in a large number of different domains over the last 15 years, ranging from investment decision analysis for companies to public decision support for local governments.
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| 7. |
- Täckström, Oscar, et al.
(författare)
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Uncertainty Detection as Approximate Max-Margin Sequence Labelling
- 2010
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Ingår i: CoNLL 2010. - : Association for Computational Linguistics. ; , s. 84-91
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Konferensbidrag (refereegranskat)abstract
- This paper reports experiments for the CoNLL 2010 shared task on learning to detect hedges and their scope in natural language text. We have addressed the experimental tasks as supervised linear maximum margin prediction problems. For sentence level hedge detection in the biological domain we use an L1-regularised binary support vector machine, while for sentence level weasel detection in the Wikipedia domain, we use an L2-regularised approach. We model the in-sentence uncertainty cue and scope detection task as an L2-regularised approximate maximum margin sequence labelling problem, using the BIO-encoding. In addition to surface level features, we use a variety of linguistic features based on a functional dependency analysis. A greedy forward selection strategy is used in exploring the large set of potential features. Our official results for Task 1 for the biological domain are 85.2 F1-score, for the Wikipedia set 55.4 F1-score. For Task 2, our official results are 2.1 for the entire task with a score of 62.5 for cue detection. After resolving errors and final bugs, our final results are for Task 1, biological: 86.0, Wikipedia: 58.2; Task 2, scopes: 39.6 and cues: 78.5.
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| 8. |
- Zhang, Jin, et al.
(författare)
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Deep Learning-Based Conformal Prediction of Toxicity
- 2021
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 61:6, s. 2648-2657
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Tidskriftsartikel (refereegranskat)abstract
- Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.
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| 9. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 10. |
- Laskowski, Kornel, 1972-, et al.
(författare)
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On the dynamics of overlap in multi-party conversation
- 2012
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Ingår i: INTERSPEECH 2012. - Portland, USA : Curran Associates, Inc. - 9781622767595 ; , s. 846-849
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Konferensbidrag (refereegranskat)abstract
- Overlap, although short in duration, occurs frequently in multi- party conversation. We show that its duration is approximately log-normal, and inversely proportional to the number of simul- taneously speaking parties. Using a simple model, we demon- strate that simultaneous talk tends to end simultaneously less frequently than in begins simultaneously, leading to an arrow of time in chronograms constructed from speech activity alone. The asymmetry is significant and discriminative. It appears to be due to dialog acts which do not carry propositional content, and those which are not brought to completion.
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