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Events for December 05, 2022
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Six Sigma Black BeltMon, Dec 05, 2022 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: USC Viterbi School of Engineering's Six Sigma Black Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Black Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Black Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's Black belt exam (administered on the final day of the course).
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Registered Attendees
Contact: Corporate and Professional Programs
Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
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PhD Defense- Sebastien Arnold
Mon, Dec 05, 2022 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
University Calendar
Committee: Maja Mataric, Fei Sha, Salman Avestimehr, Jesse Thomason, Stefanos Nikolaidis.
Title: Quickly solving new tasks, with meta-learning and without
- Date: Monday 12/5 at 10am PT on
Zoom: https://usc.zoom.us/j/96456583921?pwd=VHZTbTRZYnAzSkErY2RzenpSY1ZGZz09
- Abstract (shortened):
The success of modern machine learning (ML) stems from the unreasonable effectiveness of large data. But what about niche tasks with limited data? Some methods are able to quickly solve those tasks by first pretraining ML models on many generic tasks in a way that lets them quickly adapt to unseen new tasks. Those methods are known to ``learn how to learn'' and thus fall under the umbrella of meta-learning. While meta-learning can be successful, the inductive biases that enable fast adaptation remain poorly understood.
This thesis takes a first step towards an understanding of meta-learning, and reveals a set of guidelines which help design novel and improved methods for fast adaptation. Our core contribution is a study of the solutions found by meta-learning. We uncover the working principles that let them adapt so quickly: their parameters partition into three groups, one to compute task-agnostic features, another for task-specific features, and a third that accelerates adaptation to new tasks.
Building on those insights we introduce several methods to drastically speed up adaptation.
We propose Kronecker-factored meta-optimizers which significantly improve post-adaptation performance of models that are otherwise too small to meta-learn. We also show how to apply our insights to a visual reinforcement learning setting where meta-learning is impractical. Freezing task-agnostic parameters and adapting task-specific ones with policy-induced self-supervision enables adaptation to unseen tasks with large feature extractors pretrained on generic vision datasets.
WebCast Link: https://usc.zoom.us/j/96456583921?pwd=VHZTbTRZYnAzSkErY2RzenpSY1ZGZz09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Yuchen Lin
Mon, Dec 05, 2022 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Yuchen Lin
Title: Evaluating and Improving the Commonsense Reasoning Ability of Language Models
Committee: Xiang Ren (chair), Ram Nevatia, Yan Liu, Toby Mintz
Date & Time: Dec 5th (Monday) from 10:00 AM to 12:00 PM.
Zoom: https://usc.zoom.us/j/91622202680?pwd=cUFNbzY2OXYyTFpuaFVIZHlTWEtLUT09
Abstract:
Large pre-trained language models have become the foundation models for natural language processing. Some LMs (e.g., GPT-3) show the potential to acquire general language intelligence. However, we find that they can still make mistakes because they lack commonsense knowledge and reasoning ability, which are of vital significance in developing human-level general AI systems. In this talk, I will introduce how we can better evaluate and improve the commonsense reasoning (CSR) ability of LMs. Prior works mainly use mask-based probing and multiple-choice QA for evaluation. Their limitations prevent us from comprehensively measuring the CSR ability of LMs. To this end, I will present several benchmarks that aim to measure CSR ability in terms of open-endedness, generalization, and robustness, which are three key dimensions that are missing from the prior evaluation protocols. Then, I will introduce CSR methods that improve LMs by incorporating external knowledge. The external knowledge can be either structured graphs (e.g., ConceptNet) or unstructured text (e.g., GenericsKB), or even implicit as input-output pairs. Finally, I will briefly introduce a few interesting future directions for CSR.
WebCast Link: https://usc.zoom.us/j/91622202680?pwd=cUFNbzY2OXYyTFpuaFVIZHlTWEtLUT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Thesis Proposal - Jingyao Ren
Mon, Dec 05, 2022 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Jingyao Ren
Committee: Sven Koenig, Gaurav Sukhatme, Stefanos Nikolaidis, Feifei Qian(ECE Department), Nora Ayanian(Brown University)
Title: Algorithm Selection and Empirical Hardness of Multi Agent Pathfinding Problems
Abstract:
Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization. While many algorithms have been developed to solve MAPF problems, there is no dominating optimal MAPF algorithm that works well in all types of problems and no standard guidelines for when to use which algorithm.
In this work, we develop the deep convolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor), which takes a MAPF problem instance and attempts to select the fastest algorithm to use from a portfolio of algorithms. We improve the performance of our model by including single-agent shortest paths in the instance embedding given to our model and by utilizing supplemental loss functions in addition to a classification loss. We evaluate our model on a large and diverse dataset of MAPF instances, showing that it outperforms all individual algorithms in its portfolio as well as the state-of-the-art optimal MAPF algorithm selector. We also provide an analysis of algorithm behavior in our dataset to gain a deeper understanding of optimal MAPF algorithms' strengths and weaknesses to help other researchers leverage different heuristics in algorithm designs. Several ongoing projects are also proposed affiliated with detailed analysis such as using more advanced MAPF instance encoding techniques, Graph Neural Network based approach and utilizing the empirical hardness of MAPF to boost the performance of algorithm selectors.
This proposal will be hosted virtually. Zoom link: https://usc.zoom.us/j/6164522905
WebCast Link: https://usc.zoom.us/j/6164522905
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
