Training Data

Training data is crucial for machine learning model development, with current research focusing on improving data quality, efficiency, and mitigating biases. Active areas include generating synthetic data to address scarcity or privacy concerns, developing algorithms to optimize data selection and usage (e.g., self-paced learning, active learning), and mitigating issues like data contamination and imbalance through techniques such as data augmentation, selective parameter merging, and novel loss functions. The quality and characteristics of training data significantly impact model performance, generalization, and robustness, influencing various applications from natural language processing and image recognition to scientific computing and medical diagnosis.

Papers

August 31, 2022

Efficient Methods for Natural Language Processing: A Survey
Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
Natural Language Processing Timely Survey Training Data Efficient Method

August 26, 2022

Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
Jichao Zhang, Aliaksandr Siarohin, Yahui Liu, Hao Tang, Nicu Sebe, Wei Wang
Generative Model Training Data GAN Model Face Generation Facial Attribute Editing Generative Neural Radiance Field

August 23, 2022

Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy
Training Data Inference Cost Lottery Ticket Sparse Subnetworks Dense Network Robust Ticket Naive Ensemble

August 22, 2022

FedOS: using open-set learning to stabilize training in federated learning
Mohamad Mohamad, Julian Neubert, Juan Segundo Argayo
Data Set Training Data Open Set Statistical Model Locality Sensitive Privacy Constraint

August 16, 2022

Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud
Priya Mehta, Sandeep Kumar, Ravi Kumar, Ch. Sobhan Babu
Training Data Constructive Approach Ground Truth Latent Representation Outlier Detection Feature Enhancement Global Outlier Credit Card Fraud

August 15, 2022

A Tool for Neural Network Global Robustness Certification and Training
Zhilu Wang, Yixuan Wang, Feisi Fu, Ruochen Jiao, Chao Huang, Wenchao Li, Qi Zhu
Training Data BRIO Tool Robustness Certification Global Robustness State of the Art Robustness

August 14, 2022

Teacher Guided Training: An Efficient Framework for Knowledge Transfer
Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar
Generative Model Training Data Knowledge Transfer Generalization Bound Gradient Method Efficient Framework

August 9, 2022

Training Overparametrized Neural Networks in Sublinear Time
Yichuan Deng, Hang Hu, Zhao Song, Omri Weinstein, Danyang Zhuo
Neural Network Training Data Stochastic Gradient Descent Convergence Rate Sublinear Time Per Iteration

August 5, 2022

3D Pose Based Feedback for Physical Exercises
Ziyi Zhao, Sena Kiciroglu, Hugues Vinzant, Yuan Cheng, Isinsu Katircioglu, Mathieu Salzmann, Pascal Fua
Training Data Graph Convolutional Network Learning Framework Physical Activity

August 3, 2022

July 27, 2022

July 22, 2022

July 21, 2022

Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning
Yusuf Nasir, Louis J. Durlofsky
Deep Reinforcement Learning Training Data Flood Model

Training Data

Papers

Efficient Methods for Natural Language Processing: A Survey

Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation

Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost

FedOS: using open-set learning to stabilize training in federated learning

Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud

A Tool for Neural Network Global Robustness Certification and Training

Teacher Guided Training: An Efficient Framework for Knowledge Transfer

Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP

Comparison and Analysis of New Curriculum Criteria for End-to-End ASR

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning

Training Overparametrized Neural Networks in Sublinear Time

3D Pose Based Feedback for Physical Exercises

Gradient descent provably escapes saddle points in the training of shallow ReLU networks

Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data

Membership Inference Attacks via Adversarial Examples

NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation

Neural-Sim: Learning to Generate Training Data with NeRF

Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion

Impact of RoCE Congestion Control Policies on Distributed Training of DNNs

Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning