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web:publications [2021/03/23 10:18]
arman |
web:publications [2021/03/23 12:13]
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| ====== Peer-reviewed publications ====== | ====== Peer-reviewed publications ====== |
| * **Mix2Vec: Unsupervised Mixed Data Representation** \\Zhu C., Zhang Q., Cao L., Abrahamyan A. (2020) \\ | * **Mix2Vec: Unsupervised Mixed Data Representation** \\ Zhu C., Zhang Q., Cao L., Abrahamyan A. (2020) \\ IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), Sydney, NSW, Australia, 2020, pp. 118-127. ++ More | //Abstract.// Unsupervised representation learning on mixed data is highly challenging but rarely explored. It has to tackle significant challenges related to common issues in real-life mixed data, including sparsity, dynamics and heterogeneity of attributes and values. This work introduces an effective and efficient unsupervised deep representer called Mix2Vec to automatically learn a universal representation of dynamic mixed data with the above complex characteristics. Mix2Vec is empowered with three effective mechanisms: random shuffling prediction, prior distribution matching, and structural informativeness maximization, to tackle the aforementioned challenges. These mechanisms are implemented as an unsupervised deep neural representer Mix2Vec. Mix2Vec converts complex mixed data into vector space-based representations that are universal and comparable to all data objects and transparent and reusable for both unsupervised and supervised learning tasks. Extensive experiments on four large mixed datasets demonstrate that Mix2Vec performs significantly better than state-of-the-art deep representation methods. We also empirically verify the designed mechanisms in terms of representation quality, visualization and capability of enabling better performance of downstream tasks. |
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| | [[https://doi.org/10.1109/DSAA49011.2020.00024]] |
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| * **Reliability of TMS Phosphene Threshold Estimation: Toward a Standardized Protocol** \\ Mazzi C., Savazzi, S., Abrahamyan, A., Ruzzoli, M. (2017) \\ Brain Stimulation. Available online 2 February. ++ More | \\ //Abstract.// **Background** | * **Reliability of TMS Phosphene Threshold Estimation: Toward a Standardized Protocol** \\ Mazzi C., Savazzi, S., Abrahamyan, A., Ruzzoli, M. (2017) \\ Brain Stimulation. Available online 2 February. ++ More | \\ //Abstract.// **Background** |
| Based on our results, researchers and clinicians can estimate phosphene threshold according to MOCS or REPT equally reliably, depending on their specific investigation goals. We suggest several important factors for consideration when calculating phosphene thresholds and describe strategies to adopt in experimental procedures. | Based on our results, researchers and clinicians can estimate phosphene threshold according to MOCS or REPT equally reliably, depending on their specific investigation goals. We suggest several important factors for consideration when calculating phosphene thresholds and describe strategies to adopt in experimental procedures. |
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| \\ [[http://dx.doi.org/10.1016/j.brs.2017.01.582]] | \\ [[https://dx.doi.org/10.1016/j.brs.2017.01.582]] |
| \\ {{:pdf:mazzi_et_al_brainstim_2017.pdf|PDF}} | \\ {{:pdf:mazzi_et_al_brainstim_2017.pdf|PDF}} |
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| //Abstract.// | //Abstract.// |
| When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice-history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject’s default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics. | When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice-history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject’s default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics. |
| \\ [[http://doi.org/10.1073/pnas.1518786113|doi.org/10.1073/pnas.1518786113]] | \\ [[https://doi.org/10.1073/pnas.1518786113|doi.org/10.1073/pnas.1518786113]] |
| \\ {{:pdf:pnas_2016.pdf|PDF}} | \\ {{:pdf:pnas_2016.pdf|PDF}} |
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