Learning Generalizable

Learning generalizable models focuses on developing machine learning systems that perform well on unseen data or tasks, going beyond simple memorization of training examples. Current research emphasizes techniques like contrastive learning, knowledge distillation, and meta-learning, often implemented using transformer networks, convolutional neural networks, or graph neural networks, to achieve this goal. This pursuit is crucial for building robust and reliable AI systems applicable across diverse real-world scenarios, impacting fields ranging from medical image analysis and robotics to electronic design automation and natural language processing. The ultimate aim is to create models that are not only accurate but also adaptable and transferable, reducing the need for extensive retraining on new data.

Papers

October 25, 2024

Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
Weikai Li, Ding Wang, Zijian Ding, Atefeh Sohrabizadeh, Zongyue Qin, Jason Cong, Yizhou Sun
Expert Knowledge Field Programmable Gate Array Critical Synthesis High Level High Level Synthesis Multi FPGA Learning Generalizable Hierarchical Mixture MoE FFD

October 18, 2024

G-NeuroDAVIS: A Neural Network model for generalized embedding, data visualization and sample generation
Chayan Maitra, Rajat K. De
Generative Model Raw Data Variational Autoencoder Neural Network Model Generative Approach Interactive Visualization Learning Generalizable Sample Generation

June 17, 2024

May 1, 2024

A Self-explaining Neural Architecture for Generalizable Concept Learning
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
Contrastive Learning Concept Learning Learning Generalizable Concept Alignment Self Explaining Neural Network Saliency Network

April 7, 2024

DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
Valentin Koch, Sophia J. Wagner, Salome Kazeminia, Ece Sancar, Matthias Hehr, Julia Schnabel, Tingying Peng, Carsten Marr
Foundation Model Single Cell Cell Classification Learning Generalizable DiNO Mix Single Cell Image Blood Cell Image Bone Marrow

March 2, 2024

Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits
Chenhui Deng, Zichao Yue, Cunxi Yu, Gokce Sarar, Ryan Carey, Rajeev Jain, Zhiru Zhang
Potential Scalability Graph Attention Self Attention Module Large Scale Circuit Logic Synthesis Learning Generalizable Circuit Representation

January 11, 2024

Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
Na Wang, Lei Qi, Jintao Guo, Yinghuan Shi, Yang Gao
Target Domain Domain Invariant Representation Learning Generalizable Correlation Potential

December 8, 2023

Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment
Suhas Srinath, Shankhanil Mitra, Shika Rao, Rajiv Soundararajan
Zero Shot Contrastive Loss Image Quality Assessment Reference Image Quality Assessment Learning Generalizable Quality Aware

December 7, 2023

Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games
Ruoyao Wang, Peter Jansen
Text Based Game Learning Generalizable Text Benchmark Web Crawler

October 25, 2023

Learning Generalizable Program and Architecture Representations for Performance Modeling
Lingda Li, Thomas Flynn, Adolfy Hoisie
Performance Prediction Learning Generalizable Performance Model

October 5, 2023

Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations
Jiachi Liu, Liwen Wang, Guanting Dong, Xiaoshuai Song, Zechen Wang, Zhengyang Wang, Shanglin Lei, Jinzheng Zhao, Keqing He, Bo Xiao, Weiran Xu
Empirical Study Slot Filling Real World Noisy Datasets Learning Generalizable Input Perturbation Noise Based

September 4, 2023

Adapting Classifiers To Changing Class Priors During Deployment
Natnael Daba, Bruce McIntosh, Abhijit Mahalanobis
Supervised Learning Simple Classifier Software Deployment Learning Generalizable Class Prior Adaptive Classification

July 17, 2023

Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks
Steffen Seitz, Thomas Götz, Christopher Lindenberg, Ronald Tetzlaff, Stephan Schlegel
Neural Network Abnormal Signal Learning Generalizable Biomedical Signal Partial Discharge

July 14, 2023

Generalizable Embeddings with Cross-batch Metric Learning
Yeti Z. Gurbuz, A. Aydin Alatan
Deep Metric Learning Unseen Class Learning Generalizable Prototype Selection Global Pooling

July 6, 2023

Multi-Similarity Contrastive Learning
Emily Mu, John Guttag, Maggie Makar
Contrastive Learning Contrastive Loss Learning Generalizable

April 18, 2023

Optimal PAC Bounds Without Uniform Convergence
Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, Nikita Zhivotovskiy
Sample Complexity Statistical Learning Theory Learning Generalizable Uniform Convergence

March 13, 2023

Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu
Pre Trained Model Adaptive Importance Class Incremental Learning Stronger Generalizability Large Pre Trained Model Model Adaptation Learning Generalizable ImageNet E

December 26, 2022

Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
Jianda Chen, Sinno Jialin Pan
Reinforcement Learning Meta Learning State Representation Dimensional State Space Learning Generalizable Behavioral Similarity

October 14, 2022

Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee
Deep Learning Knowledge Distillation Distribution Sample Vehicle Routing Problem Learning Generalizable Cross Distribution Generalization

Learning Generalizable

Papers

Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis

G-NeuroDAVIS: A Neural Network model for generalized embedding, data visualization and sample generation

Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding

Text Grafting: Near-Distribution Weak Supervision for Minority Classes in Text Classification

A Self-explaining Neural Architecture for Generalizable Concept Learning

DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology

Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits

Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations

Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment

Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games

Learning Generalizable Program and Architecture Representations for Performance Modeling

Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations

Adapting Classifiers To Changing Class Priors During Deployment

Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks

Generalizable Embeddings with Cross-batch Metric Learning

Multi-Similarity Contrastive Learning

Optimal PAC Bounds Without Uniform Convergence

Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need

Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities

Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation