Learning

### Omnidirectional Lifelong Learning via Ensembling Representations

![:scale 40%](images/neurodata_blue.png)

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### Different Learning Paradigms

- biological learning: continually use new data to improve performance on both .ye[past] and potential .ye[future] tasks
- (classical) machine learning: .ye[tabula rasa], forget old tasks with new data

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### Existing strategies

- Given new data, either
  - forward transfer: improve performance on .ye[future tasks] given current data 
  - avoid catastrophic forgetting: do not .ye[forget past] tasks
- These goals are .ye[inadequate]
- We desire .ye[synergistic learning]: transfer both forward and backward

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### Our approach: Ensembling Representations

![:scale 100%](images/learning_schema_new.png)

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### Omnidirectional Algorithms can Transfer Between XOR and XNOR

![:scale 100%](images/xor_xnor_exp.png)
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### CIFAR 10x10

.pull-left[
- *CIFAR 100* is a popular image classification dataset with 100 classes of images. 
- 500 training images and 100 testing images per class.
- All images are 32x32 color images.
- CIFAR 10x10 breaks the 100-class task problem into 10 tasks, each with 10-class.
]

.pull-right[
<img src="images/l2m_18mo/cifar-10.png" style="position:absolute; left:450px; width:400px;"/>
]

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### Omnidirectional Algorithms Show Forward Transfer for the CIFAR 10x10 Tasks
![:scale 115%](images/cifar_exp_fte.png)

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### Omnidirectional Algorithms Uniquely Show Backward Transfer for Each CIFAR 10x10 Task
![:scale 115%](images/cifar_exp_bte.png)

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### Acknowledgements

<!-- <div class="small-container">
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  <div class="centered">Eric Bridgeford</div>
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  <div class="centered">Ben Pedigo</div>
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  <div class="centered">Jaewon Chung</div>
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##### JHU

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  <div class="centered">Carey Priebe</div>
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  <div class="centered">Drishti Mannan</div>
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  <div class="centered">Jesse Patsolic</div>
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  <div class="centered">Meghana Madhya</div>
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  <div class="centered">Hayden Helm</div>
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  <div class="centered">Richard Gou</div>
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  <div class="centered">Ronak Mehta</div>
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  <div class="centered">Jayanta Dey</div>
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  <div class="centered">Will LeVine</div>
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##### Microsoft Research

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  <div class="centered">Chris White</div>
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  <div class="centered">Weiwei Yang</div>
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  <div class="centered">Jonathan Larson</div>
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  <div class="centered">Bryan Tower</div>
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##### DARPA L2M

{[BME](https://www.bme.jhu.edu/),[CIS](http://cis.jhu.edu/), [ICM](https://icm.jhu.edu/), [KNDI](http://kavlijhu.org/)}@[JHU](https://www.jhu.edu/) | [neurodata](https://neurodata.io)
<br>
[jovo@jhu.edu](mailto:j1c@jhu.edu) | <http://neurodata.io/talks> | [@neuro_data](https://twitter.com/neuro_data)

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.footnote[Questions?]