Accelerated Free-Form Model Discovery of Interpretable Models using Small Data
50 mins 55 secs,
741.71 MB,
MPEG-4 Video
640x360,
29.97 fps,
44100 Hz,
1.94 Mbits/sec
Share this media item:
Embed this media item:
Embed this media item:
About this item
| Description: |
Horesh, L
Tuesday 31st October 2017 - 11:10 to 12:00 |
|---|
| Created: | 2017-11-02 13:39 |
|---|---|
| Collection: | Variational methods and effective algorithms for imaging and vision |
| Publisher: | Isaac Newton Institute |
| Copyright: | Horesh, L |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
|
| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | The ability to abstract the behavior of a system or a phenomenon and distill it into a consistent mathematical model is instrumental for a broad range of applications. Historically, models were manually derived in a first principles fashion. The first principles approach often offers the derivation of interpretable models of remarkable levels of universality using little data. Nevertheless, their derivation is time consuming and relies heavily upon domain expertise. Conversely, with the rising pervasiveness of data-driven approaches, the rapid derivation and deployment of models has become a reality. Scalability is gained through dependence upon exploitable structure (functional form). Such structures, in turn, yield non-interpretable models, require Big Data for training, and provide limited predictive power outside the training set span. In this talk, we will introduce an accelerated model discovery approach that attempts to bridge between the two conducts, to enable the discovery of universal, interpretable models, using Small Data. To accomplish that, the proposed algorithm searches for free-form symbolic models, where neither the structure nor the set of operator primitives are predetermined. The discovered models are provably globally optimal, promoting superior predictive power for unseen input. Demonstration of the algorithm in re-discovery of some fundamental laws of science will be provided, and references to on-going work in the discovery of new models for, hitherto, unexplainable phenomena. |
|---|---|
Available Formats
| Format | Quality | Bitrate | Size | |||
|---|---|---|---|---|---|---|
| MPEG-4 Video * | 640x360 | 1.94 Mbits/sec | 741.71 MB | View | Download | |
| WebM | 640x360 | 677.95 kbits/sec | 252.91 MB | View | Download | |
| iPod Video | 480x270 | 522.3 kbits/sec | 194.78 MB | View | Download | |
| MP3 | 44100 Hz | 249.79 kbits/sec | 93.25 MB | Listen | Download | |
| Auto | (Allows browser to choose a format it supports) | |||||