CS 330: Deep Multi-Task and Meta Learning

Reference list of projects from previous offerings

Title Students
Transformer based multi-task learning for predicting student coding performance Sai Vineeth Kalluru Srinivas, Yijing Bai
Exploring the Role of Novel Class Detection in Few-shot Detection Xinran Zhao, Xiaoyuan Ni
Representation Search in Neural Space for Image Compression ZhuoYi Cai
Improving Few-Shot Learning Performance by Fine-Tuning Late Layers Wenxin Dong, Kevin Lit
Few Shot Learning with a FiLM-Generator Network Ethan Cheng
Few-shot Multimodal Multitask Multilingual Learning Aman Chadha
Meta-Learning for Better Learning: Automatically Tagging Learning Goals Amir Zur
Emotion-Cause Pair Extraction with Hybrid Architecture Analysis Ta-Hsuan Chao, Yi-Chia Wu, Pei-Wei Kao
Looking Big-Picture: Leveraging a Few Demos to Learn from Unlabeled Play Data Maximilian Du
Improving Sub-group Robustness for EEG Seizure Detection Yixing Jiang
Online Meta-learning for time series data Aman Bansal, Pranay Reddy Samala, Shubham Anand Jain
Domain- Aware Mental Health Virtual Assistant Yunan Li, Heidi Zhang, Lynette Gao
Meta Learning for Few-Shot Object Detection Shunyao Xu, Zhiling Huang, Shenghan Chen
Few-shot classification of drug target activity augmented with pretrained protein embeddings David Huang, Tom Pritsky, David Kuo
DABS: JAX MAE Implementation & Cross-Domain Transfer Learning Eden Y Wang
Implict MAML based SDFs Samaksh Goyal
Model Fine-tuning Layer Prediction for Surgical Fine-tuning Tasks Angela Liu
Multitask learning from egocentric infant headcam data Alvin Wei Ming Tan
Prediction of Rare Metastatic Cancer from RNA Data in Few-Shot Settings Mohammad (Jabs) Aljubran, Alaskar Alizada
Diffusion Models as Visual Reasoners Maya Sharanya Srikanth, Jason Jiachen Lin
Can Systematic Manipulation of Compositional Shared Substructure of Tasks Change the "Difficulty" of a Meta-Learning task? Mohammad Ali Rehan, Pratyush Agarwal, Saumya Goyal
Synthetic Data Generation for Few-Shot Learning Ofure Mary Ebhomielen, Vaish Shrivastava, Rajan Pathe Vivek
Conservative Out-of-Distribution Detection Caroline Choi
Solving the Camouflage Object Segmentation Problem with Multitask-Learning and MAML Augmented SINet Xin Zhang, William L Cai
Task Fusion: Specializing Common Representations in Multi-Task Learning Dennis J Duan, Ayush Singla, Ananth Agarwal
Global Health Monitoring From Satellite Data Through Multi-Task Learning Sharmila Nangi, Nick Tantivasadakarn
Garbage Classification Using Multi Task Learning Goutham Krishna Teja Muppala
Generating Adversarial Meta-Learning Tasks via Noise Memorization Daniel David Richman, Shai Limonchik
Using Safe RL to Generalize the Minimum Spanning Tree Problem Anthony Tzen, Jensen Hwa, Ali Teshnizi
Bringing Multitask Learning in identifying the existence of Social Determinant of Health in Clinical Reports Ranajit Gangopadhyay
Layer Autotuning: Automated Fine-tuning Algorithms For Transfer Learning Shaunak Bhandarkar, Kai Mica Fronsdal, Kelechi Uhegbu
Application and Study of Meta Learning on Land Cover Classification and Crop Prediction Chuanqi Chen, Hongxu Ma, Chung Ching Cheung
Meta-learning approaches to bird species classification Justine Breuch, Cameron David Tew, Miles Richard Hutson
Quantifying Diversity of Large Language Model Pretraining Datasets for Studying Emergence of In-Context Learning Alycia Lee
History-Aware Perceiver-Actor For Multi-Task Robot Manipulation Ziang Liu, Blair Huang
Essays: New Approaches to Multi-task and Transfer Learning for Long SequenceTexts and Complex Inter-task Relationships Michael Hardy
Improving Blood Glucose Prediction Using Meta Learning Akshat Nath Mishra
Few-Shot Handwritten Chinese Character Classification Eric Chen
Application of Prototypical networks with Few-Shot Learning for Art Analysis and Classification Lara Malinov
Urban Land Cover for Water Conservation and Management Brandon Wada
Meta-Learning to Mitigate Noise Propagation in Gradual Self-Training Angela Tsao
Mapping Visual Features to Differences in Operative Skill, Intraoperative Events, and Post-Operative Outcomes for Laparoscopic Cholecystectomy Shelly Goel
Automatic fine-tuning Nipun Agarwala, Neal Rakesh Vaidya, Haider Suleman
Distribute Hierarchical Meta Reinforce Learners Mustafa Bayramov
Building a Robust Rare Activities Detection System with Limited Data Natasha Ong, Mac Klinkachorn, Jeffrey Halim
Coefficient Based Fine-tuning of Language Models on Distribution Shifts Suma Kasa, Antoine Bigeard, Samuel Barry
Learning to Augment Chang Li, Mingshan Wang, Wen Xu
TimeGraphs: Meta-Learning over Time using Graph Neural Networks Paridhi Maheshwari
MAMLSS: Extending Self-Supervision to Model Agnostic Meta-learning Ishira U Fernando, Michael Jonathan Atkin
Conditioning Generative Language Models for Finetunability Matt Smith
Prototypes, Contrastive Learning And Classification Under Novelty Advaya Gupta, Jinang Rupeshkumar Shah, Aditya Ashwini Agrawal
Adapting Large Language Models to Solve Word Puzzles with Shared Intermediate Steps Jing Huang
Learning Virtual Machine Duration for Resource Allocation in the Cloud Computing Environment Madhumita Vijay Dange, Yuwen Yang
Image Compression using Implicit Neural Representation Huafan Cai
Does surgical fine-tuning work in NLP and does it reflect the classical N L P pipeline? Pooja Sethi
Specializing Common Representations in Multi-Task Learning Tom Keohane Murray, Silviu Ionut Lazar, Luke Hein Martin
Meta-learning for digital audio effects Lucy Lu, Dirk Stallard Roosenburg, Ivan Villa-Renteria
Increased Concept Diversity Improves In-Context Learning on Unseen Concepts Haishan Gao
Analyzing latent representations in trained MAMLs Ryan Lian, Sri Jaladi, Victor Stilianov Kolev
Gradient-Based Meta Learning for Morphologically Diverse Few-Shot Cell Segmentation Rohan Sikand
Surgical Fine-tuning, Automated Surgical Fine-tuning Metrics Muhammad Ghazi Randhawa
Studying the Robustness of Transformer-based Imitation Learning to Domain Shift Nick Soulounias, Sidhart Krishnan, Avidesh F Marajh
Bias Transfer from Source Datasets when Fine-tuning Nicole Meister
Instance Specific Data Augmentation for meta-learning Pranay Agrawal, Eric Tang
Using Multiple Task and Meta-Learning Networks for Board-Based Game Strategy Development Ian Yue Han Ng, Brian Y Wu, Benjamin Bin Yan
Meta-Learning Optimal Simulator Fidelity Settings For Autonomous System Validation Marc Schlichting
Offline Meta-Reinforcement Learning: Extending MACAW Daniel Havir
PIGEON: Predicting Image Geolocations Michal Skreta, Lukas Haas, Silas Alberti
Semi Supervised Meta Learning for Spatiotemporal Learning Pratyush Muthukumar, Faraz Waseem, Muhammad Shahir Rahman
Meta-Learning to Locally Predict Free-Flight Potential Drew Wadsworth
Studying the Robustness of Transformer-based Imitation Learning to Domain Shift Axel Peytavin, Leqi Zeng, Matthew Thomas
TLDChoiceNet: Quantitatively Choosing a Transfer Learning Dataset James Duncan Braza, Anna NING
Convex prototypical networks with ReLU activation Devin Ardeshna
Meta Learning on Comments for Code Comprehension Aditya Chandrasekar
A cost efficient scalable method to extend to low-resource languages for ASR models Yuxin Ding
Learning Protein Functional Annotation from Few-Shot Data via Meta-Learning Aditya Gulati, Keerti Kareti
Multimodal Self-Pretraining for Medical Image Classification Cara Elise Van Uden, Gordon Downs, Ben Alexander
Meta-Learning for Electronic Health Records Rishi Agarwal, Tathagat Verma
Parameter Level Surgical Fine-Tuning for Transformers Siyan Sylvia Li, Kathy Yu
MLETA: Meta Learning for Efficient Test-time Adaptation Ansh Khurana, Soumya Chatterjee, Aman Kansal
Self-Driving Web Browsers Bryan Chiang
Meta-learning for multi-horizon limit order book return prediction Charles Congyue Chen
3KG v2: Learning universal electrocardiogram representations for label-efficient phenotype discovery Bryan Dev Gopal
Instance-Specific Augmenter Using Representation Matching Rajas Bansal, Atharva Amdekar, Anuj Nagpal
Meta-Learning Independent Representations for Counterfactual Time Series Generation Sven Lerner, Greg Zanotti
Class imbalance in meta-test support sets Ajay Kannan
Augmentation by Distillation Sanjit Neelam
Few-shot QA tasks Adil Sadik
Gradient-Informed Branching For Multi-Task Learning Gautam Mittal, Leni Ven, Zedian Xiao
Meta-GAT: Few Shot Node Classification with Graph Attention Networks Nabil Ahmed
Automatic Selective Layer-Wise Task-Specific Fine-Tuning Improves Transfer Learning Chunming Peng, Matt Peng, Rongbin Li
Domain Adaptive Learning on Brain Networks David Dai
Expert Affinity Grouping for Multi-Task Learning Peter Michael Paquet
Categorized and Self-diagnose Neural Network approach for Continual learning Rong Chen
MetaAugNet: Learning Data Augmentations on the Fly Alex Zachary Fan, Brian Wesley Hill, Yoko Nagafuchi
Single Life Reinforcement Learning with Quadruped Robots Govind Chada
Enriching Model Representation With Adaptive Modulated Learning Yibo Zhang
Apply Task-adaptive Inner-loop Loss Tuning in MAML Jiwen Chen
Meta-Learning Force Fields David J Toomer
Few-Shot Speaker Identification Using Masked Autoencoders and Meta-Learning John Boccio
Wormhole MAML: Meta-Learning In Glued Parameter Space Yuan Gao, Beicheng Lou, Tracy Chang
Assessing Meta-Reinforcement Learning Algorithms in Complex Environments Michael Hany Elabd, Ronak Anuj Malde
How Does Parameter Sharing Affect Multi-task Learning? Tz-Wei Mo, Annie Ho, Li-Heng Lin
Towards More Robust Natural Language Prompting for Multi-Task Text Classification Violet Yao, Chenshu Zhu, Shirley Shuocheng Zhang
Enriching Model Representation With Adaptive Modulated Features For Multitask Learning Xinglong Sun, Yibo Zhang
Evaluating several methods for mixing knowledge of Vietnamese and knowledge of law Pham Thanh Huu
Self-Supervised learning for identifying fishes Aleksandr Timashov
Adapting to Unknown Conventions in Cooperative Multi-Agent RL Bidipta Sarkar
Semi-Supervised Learning of Multi-Task Contrast-Weighted Image Synthesis from Magnetic Resonance Fingerprinting Phil Michael Adamson, Mahmut Yurt
Fine-tuning a visual transformer for PV detection in varying resolutions Rajanie Prabha, Ivan Gonzalo Higuera-Mendieta
Investigating Optimal AMA Prompting Strategies for Small Foundation Models Andrew Louis Hojel, Oscar O'Rahilly, Aayush Amit Agrawal
SayAgain: Prompt-tuning for improved planning with large language models Michael John Lingelbach, Gabrael Levine
Unsupervised Real2Sim Transfer for Safe Evaluation of Autonomous Vehicles Anirudhan Badrinath
SOAR: Single-Shot Multi-Task Object Action Recognition Tiange Xiang, Zhuoyi Huang
Transfer-Learning and Fine-Tuning across Time-Series Spectrogram Tasks Xiang Jiang, Michael Gregory Campiglia, Ryan Tan
Transfer and Meta-Learning in Graph Neural Networks: A Recommender System Approach Megan Backus, Hamdy Hamoudi, Rouven Spiess
Exploring transferability and model agnostic meta learning across NLP Tasks Phillip Yao-Lakaschus, Marcos Silveira, Karen Garcia Mesa
Contrastive and Adversarial Learning for Few-shot Image Classification Gary Dai, Joanne Zhou
User Rating Prediction and Product Recommendation on Amazon Reviews Jordan Patrick Byrd, Li Tian, Sylvia Yuan
AHA-Ensemble: Automatic Heterogeneous Architecture Ensembles for Domain Generalization Javier Yu
Controlling Catastrophic Forgetting Using Feature-based Knowledge Distillation Ali H Shoeb
How to Preserve Adversarial Robustness During Transfer Learning Laura Fee Schneider, Maurice Andre Georgi
Few-Shot 3D Object Detection Kutluhan Buvukburc