AI Innovation Institute

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Experiencing Machine Learning in Collider-Accelerator Control System

Abstract: The Relativistic Heavy Ion Collider (RHIC) at Collider-Accelerator Department (C-AD) of BNL provides the world's only high-energy polarized proton beam. It is in the unique position to study where nuclei obtain their spin. During 25 years of operation at RHIC, the C-AD controls group has developed its own control system to tune the accelerator performance, which contains millions of control points. The successful operation of this system will highly affect the machine performance. RHIC's successor, the Electron-Ion Collider (EIC), will be one of the most complex scientific instruments ever built, with the capability of colliding polarized proton and electron beams. The increasing complexity of instruments will require new, sophisticated control methods/tools to tune and optimize the accelerator performance. In this talk, I will summarize some projects developed in recent years that utilize machine learning in the C-AD controls group.

Biography: Dr. Yuan Gao is an assistant scientist at the Collider-Accelerator Department (C-AD) at Brookhaven, primarily working on developing new machine learning schemes in the control group to enhance system performance. His research interests include game theory, algorithm design, anomaly detection, and simulation modeling.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604302440?pwd=0x2I95PIvbkkzIi6rA0MNnon5k2sux.1

Meeting ID: 160 430 2440
Passcode: 478223

Over the past decade, larger datasets, hardware accelerations, and network architecture improvements have contributed to phenomenal achievements in many tasks of computer

vision. However, in the absence of large datasets, computer vision models struggle to learn

general representations which results in poor performance. Few-shot learning tries to address 

this problem by proposing models which learn from a few examples.

I first give an overall review of few-shot learning methods. I particularly focus on generative Few Shot Learning(FSL) methods, which augment the scarce categories in a dataset by generating samples for those rare categories. As the actual class distribution can be complex and lie very close to each other, the sample generated for one class can be noisy or lie close to another class.  However, none of the current FS generative methods perform any form of quality control of the generated samples.

In this work, I propose to identify and remove the generated samples that are less likely to be in the distribution of the few-shot class. Here I particularly deal with few-shot scenarios where the

prior information of the relationship between the classes based on visual  similarity is available. The main idea is to exploit these priors to better identify the unreliable generated samples.

Particularly, I have proposed two methods based on class relationship to detect noisy generated samples. In the first method, we assume that the embedding space of each class follows a Gaussian distribution.  From this assumption, I propose Gaussian Neighborhood (GN), a method to estimate how likely a generated sample is drawn from the estimated distribution of a few-shot class.  We evaluate this method on the Hematopoiesis dataset.  By simply eliminating samples based on thresholding our proposed GN scores, the few-shot  classification  performance  is  improved by 5% and 2% in five shot and one shot respectively, compared to the model trained on all generated images. 

The GN scores represent the similarity distances from the generated samples to their classes, based on the assumption that each class is a Gaussian distribution.  However, this assumption might be strict in many scenarios since the real distributions of data can be arbitrarily complex.  Thus in my second proposed method, I aim to learn such similarity distances directly from data via metric learning. I propose to train a deep-network to regress the similarity distance between a pair of samples.  This network is trained using both the class-level  visual  similarity  information  and  the  class  labels.   This method improves the 1-shot and 5-shot classification performances by 0.5% and 1% respectively, compared to GN.