Training Data

By Florian Schroff, Andrew Zisserman, Antonio Criminisi. 2017

83 citations recorded by the source corpus.

Subfields: Organizational Behavior and Human Resource Management, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Organizational Behavior and Human Resource Management

1. Alexandre Passos

In Evaluation & Benchmarks

1. Nicholas Carlini
2. James Lee-Thorp
3. Xavier García
4. Brian Lester
5. J. Clark
6. Eric Wallace
7. Adam Roberts
8. Om Thakkar

Citations in the corpus

• Scaling Up Models and Data with t5x and seqio (2022, background): “This avoids repeating data on intentional or unintentional (e.g., due to preemption) training restarts, which can lead to reduced performance and memorization (Lee et al., 2022).”
• Did the Model Understand the Question? (2018, background): “Indeed, Mudrakarta et al. (2018) note that the state-of-the-art strongly supervised 6 model on WikiTableQuestions (Krishnamurthy et al., 2017) enjoys a 7% gain in its final accuracy by leveraging perturbed tables during training.”
• Measuring Forgetting of Memorized Training Examples (2022, background): “Resulting privacy concerns have led to a variety of techniques for private machine learning, including differentially private training [1, 61, 62, 25, 54], machine unlearning [5, 59, 68], and various heuristics like regularization [57], data augmentation [2] or gradient clipping [11, 73, 34].”