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<!DOCTYPE html>
<html>
<head>
<title>Learning</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
<link rel="stylesheet" href="fonts/quadon/quadon.css">
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<link rel="stylesheet" href="slides_style_i.css">
<script type="text/javascript" src="assets/plotly/plotly-latest.min.js"></script>
</head>
<body>
<textarea id="source">
<div>
<img src=
"images/darpa_logo.png"
alt="jhu logo"
align="left"
width = "100"
height= "50">
<img src=
"images/jhu.png"
alt="jhu logo"
align="right"
width = "140"
height= "65">
</div>
<br>
###Lifelong Learning: Theory and Practice
PI: Joshua T. Vogelstein, [JHU](https://www.jhu.edu/)<br>
Jayanta Dey, Ali Geisa, Hayden Helm, Ronak Mehta, Will LeVine,
Carey E. Priebe <br>
Co-PI: Vova Braverman, [JHU](https://www.jhu.edu/) <br>
Haoran Li, Aditya Krishnan, Jingfeng Wu <br>
SGs: SRI, ARGONE, HRL
<center>
</center>
---
### Summary
.ye[Research Question:] Why is LL difficult, and how can we design algorithms/datasets to solve it?
.ye[Approach:]
- Introduced out-of-distribution (OOD) learning theory framework for theoretical analysis of lifelong learning
- Introduced ensembling representations
.ye[Accomplishments:]
- Proved various OOD weak learner theorems
- Achieved consistent positive forward and backward transfer (synergistic learning) in practice
.ye[Key Take-Away:] LL is fundamentally harder than classical ML, and ensembling representations can synergistically learn
---
### Result 1: OOD Learning Theory
We uncouple the evaluation distribution from training data distributions
---
### Putting LL within OOD Framework
---
### Defining/Quantifying Learning & Forgetting
<!-- The above two definitions enable one to assess .ye[whether] an agent $f$ has learned, but not .ye[how much] it learned. -->
Using non-task data to improve performance over what it could achieve using only task data
- Learning: $\mathbf{S}^A=\mathbf{S}\_0$ and $\mathbf{S}^B=\mathbf{S}\_n$.
- Transfer learning: $\mathbf{S}^A=\mathbf{S}^1$ and $\mathbf{S}^B=\mathbf{S}\_n$.
- Multitask learning: for each $t$, $\mathbf{S}^A=\mathbf{S}^t$ and $\mathbf{S}^B=\mathbf{S}\_n$.
- Forward learning: $\mathbf{S}^A=\mathbf{S}^t$ and $\mathbf{S}^B=\mathbf{S}^{< t}$.
- Backward learning: $\mathbf{S}^A=\mathbf{S}^{< t}$ and $\mathbf{S}^B=\mathbf{S}\_n$.
<!-- Each of the previous definitions are all special cases of $LE^t_f(\mathbf{S}^A, \mathbf{S}^B)$, for specific choices of $\mathbf{S}^A$ and $\mathbf{S}^B$ -->
---
### Result 2: Proving novel properties of OOD learning
<!-- 
basically, using non-task data to improve performance at all
basically, using non-task data to perform arbitrarily well -->
<!-- --- -->
<!-- ### Weak OOD Learner Theorem -->
Classical theory:
- Weak learning: can do better than chance on some task with sufficient data
- Strong learning: can do arbitrarily close to optimal on some task with sufficient data
- Weak Learner Theorem: if a problem is weakly learnable, it is also strongly learnable
OOD learning theory
- Training distribution is uncoupled from evaluation distribution
---
### More data is inadequate for LL
Theorem 1: With *only* out-of-distribution data, there exists some problems that are weakly, but not strongly, learnable.
- This implies that OOD learning is different *in kind* from in-distribution learning.
- Lifelong learning is a special case of OOD learning
- Getting .ye[more] data is *not* guaranteed to improve performance arbitrarily in LL, we need .ye[better] data
---
### Learning efficiency is a fundamental notion of learning
Theorem 2: Weak OOD learnability implies transfer learnability (i.e., learning efficiency > 1). That is, if one can weakly learn, one can also transfer learn, but not necessarily vice versa.
- This implies that transfer learnability is a fundamental property of learning problems
- In other words, inability to transfer is equivalent to inability to learn at all.
---
### Result 3: Ensembling representations achieves synergistic learning
---
### Omnidirectional Algorithms Show Forward Transfer
CIFAR 10x10
<!-- - *CIFAR 100* is a popular image classification dataset with 100 classes of images. -->
<!-- - CIFAR 10x10 breaks the 100-class task problem into 10 tasks, each with 10-class. -->
---
### Omnidirectional Algorithms Uniquely Show Backward Transfer for Each Task
---
### Future Directions/ Transitions
- omnidirctional algorithm code continues to improve [http://proglearn.neurodata.io/](http://proglearn.neurodata.io/)
- streaming forest for streaming lifelong learning setup [https://sdtf.neurodata.io](https://sdtf.neurodata.io)
---
### Kernel Density Networks/Forests generate well calibrated posteriors
- [https://github.com/neurodata/kdg](https://github.com/neurodata/kdg)
- KDG on Guassian XOR simulation data
<br>
---
### Deep Networks are the worst model of the mind
<img src=
"images/nn_rf_jong.gif"
alt="jong"
width = "700"
height= "250">
---
<br>
<br>
<br>
<img src=
"images/vova1.png"
alt="vova 1"
width = "800"
height= "400">
---
<br>
<br>
<br>
<img src=
"images/vova2.png"
alt="vova 2"
width = "800"
height= "400">
---
### Acknowledgements
<!-- <div class="small-container">
<img src="faces/ebridge.jpg"/>
<div class="centered">Eric Bridgeford</div>
</div>
<div class="small-container">
<img src="faces/pedigo.jpg"/>
<div class="centered">Ben Pedigo</div>
</div>
<div class="small-container">
<img src="faces/jaewon.jpg"/>
<div class="centered">Jaewon Chung</div>
</div> -->
<div class="small-container">
<img src="faces/yummy.jpg"/>
<div class="centered">yummy</div>
</div>
<div class="small-container">
<img src="faces/lion.jpg"/>
<div class="centered">lion</div>
</div>
<div class="small-container">
<img src="faces/violet.jpg"/>
<div class="centered">baby girl</div>
</div>
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<img src="faces/family.jpg"/>
<div class="centered">family</div>
</div>
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<img src="faces/earth.jpg"/>
<div class="centered">earth</div>
</div>
<div class="small-container">
<img src="faces/milkyway.jpg"/>
<div class="centered">milkyway</div>
</div>
##### JHU
<div class="small-container">
<img src="faces/cep.png"/>
<div class="centered">Carey Priebe</div>
</div>
<!-- <div class="small-container">
<img src="faces/randal.jpg"/>
<div class="centered">Randal Burns</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/cshen.jpg"/>
<div class="centered">Cencheng Shen</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/bruce_rosen.jpg"/>
<div class="centered">Bruce Rosen</div>
</div>
<div class="small-container">
<img src="faces/kent.jpg"/>
<div class="centered">Kent Kiehl</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/mim.jpg"/>
<div class="centered">Michael Miller</div>
</div>
<div class="small-container">
<img src="faces/dtward.jpg"/>
<div class="centered">Daniel Tward</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/vikram.jpg"/>
<div class="centered">Vikram Chandrashekhar</div>
</div>
<div class="small-container">
<img src="faces/drishti.jpg"/>
<div class="centered">Drishti Mannan</div>
</div> -->
<div class="small-container">
<img src="faces/jesse.jpg"/>
<div class="centered">Jesse Patsolic</div>
</div>
<!-- <div class="small-container">
<img src="faces/falk_ben.jpg"/>
<div class="centered">Benjamin Falk</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/kwame.jpg"/>
<div class="centered">Kwame Kutten</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/perlman.jpg"/>
<div class="centered">Eric Perlman</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/loftus.jpg"/>
<div class="centered">Alex Loftus</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/bcaffo.jpg"/>
<div class="centered">Brian Caffo</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/minh.jpg"/>
<div class="centered">Minh Tang</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/avanti.jpg"/>
<div class="centered">Avanti Athreya</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/vince.jpg"/>
<div class="centered">Vince Lyzinski</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/dpmcsuss.jpg"/>
<div class="centered">Daniel Sussman</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/youngser.jpg"/>
<div class="centered">Youngser Park</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/shangsi.jpg"/>
<div class="centered">Shangsi Wang</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/tyler.jpg"/>
<div class="centered">Tyler Tomita</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/james.jpg"/>
<div class="centered">James Brown</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/disa.jpg"/>
<div class="centered">Disa Mhembere</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/gkiar.jpg"/>
<div class="centered">Greg Kiar</div>
</div> -->
<!-- <div class="small-container">
<img src="faces/jeremias.png"/>
<div class="centered">Jeremias Sulam</div>
</div> -->
<div class="small-container">
<img src="faces/meghana.png"/>
<div class="centered">Meghana Madhya</div>
</div>
<!-- <div class="small-container">
<img src="faces/percy.png"/>
<div class="centered">Percy Li</div>
</div>
-->
<div class="small-container">
<img src="faces/hayden.png"/>
<div class="centered">Hayden Helm</div>
</div>
<div class="small-container">
<img src="faces/rguo.jpg"/>
<div class="centered">Richard Gou</div>
</div>
<div class="small-container">
<img src="faces/ronak.jpg"/>
<div class="centered">Ronak Mehta</div>
</div>
<div class="small-container">
<img src="faces/jayanta.jpg"/>
<div class="centered">Jayanta Dey</div>
</div>
<div class="small-container">
<img src="faces/will.jpg"/>
<div class="centered">Will LeVine</div>
</div>
##### Microsoft Research
<div class="small-container">
<img src="faces/chwh-180x180.jpg"/>
<div class="centered">Chris White</div>
</div>
<div class="small-container">
<img src="faces/weiwei.jpg"/>
<div class="centered">Weiwei Yang</div>
</div>
<div class="small-container">
<img src="faces/jolarso150px.png"/>
<div class="centered">Jonathan Larson</div>
</div>
<div class="small-container">
<img src="faces/brtower-180x180.jpg"/>
<div class="centered">Bryan Tower</div>
</div>
##### DARPA L2M
<!-- Hava, Ben, Robert, Jennifer, Ted. -->
{[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)
</div>
<!-- <img src="images/funding/nsf_fpo.png" STYLE="HEIGHT:95px;"/> -->
<!-- <img src="images/funding/nih_fpo.png" STYLE="HEIGHT:95px;"/> -->
<!-- <img src="images/funding/darpa_fpo.png" STYLE=" HEIGHT:95px;"/> -->
<!-- <img src="images/funding/iarpa_fpo.jpg" STYLE="HEIGHT:95px;"/> -->
<!-- <img src="images/funding/KAVLI.jpg" STYLE="HEIGHT:95px;"/> -->
<!-- <img src="images/funding/schmidt.jpg" STYLE="HEIGHT:95px;"/> -->
---
background-image: url(images/l_and_v.jpeg)
.footnote[Questions?]
---
class: middle
# .center[Appendix]
---
.small[
### Publications
1. A. Geisa et al. [Towards a theory of out-of-distribution learning](https://arxiv.org/abs/2109.14501), arXiv, 2021.
1. J. T. Vogelstein et al. [Omnidirectional Transfer for Quasilinear Lifelong Learning](https://arxiv.org/abs/2004.12908), arXiv, 2021.
1. Xu, Haoyin, et al. [Streaming Decision Trees and Forests](https://arxiv.org/abs/2110.08483), arXiv, 2021.
1. C. E. Priebe et al. [Modern Machine Learning: Partition and Vote](https://doi.org/10.1101/2020.04.29.068460), 2020.
1. R Guo, et al. [Estimating Information-Theoretic Quantities with Uncertainty Forests](https://arxiv.org/abs/1907.00325). arXiv, 2019.
1. R. Perry, et al. [Manifold Forests: Closing the Gap on Neural Networks](https://openreview.net/forum?id=B1xewR4KvH). arXiv, 2019.
1. C. Shen and J. T. Vogelstein. [Decision Forests Induce Characteristic Kernels](https://arxiv.org/abs/1812.00029). arXiv, 2019.
1. M. Madhya, et al. [Geodesic Learning via Unsupervised Decision Forests](https://arxiv.org/abs/1907.02844). arXiv, 2019.
1. M. Madhya, et al. [PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment](https://arxiv.org/abs/2011.05383). arXiv, 2020.
### Conferences
1. J.T. Vogelstein et al. A biological implementation of lifelong learning in the pursuit of artificial general intelligence. NAISys, 2020.
2. B. Pedigo et al. A quantitative comparison of a complete connectome to artificial intelligence architectures. NAISys, 2020.
]
---
### Biological learning is on top
---
### Omnidirectional Algorithms can Transfer Between XOR and XNOR
---
### Spoken Digit dataset
.pull-left[
- *Spoken Digit* contains recording from 6 different speakers.
- Each digit has 50 recordings (3000 total recordings).
- For each recording spectrogram was extracted using using Hanning windows of duration 16 ms with an overlap of 4 ms.
- The spectrograms were resized down to 28×28.
]
.pull-right[
<img src="images/spectrogram.png" style="position:absolute; left:500px; width:400px;"/>
]
---
### Omnidirectional Algorithms on Spoken Digit Task
</textarea>
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