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Found 7 papers, 4 papers with code
On Influence Functions, Classification Influence, Relative Influence, Memorization and Generalization
We conclude that influence functions can be made practical, even for large scale machine learning systems, and that influence values can be taken into account by algorithms that selectively remove training points, as part of the learning process.
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?
Out-of-distribution (OOD) detection is a critical task for reliable machine learning.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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Enabling Inference Privacy with Adaptive Noise Injection
To this end, we propose Adaptive Noise Injection (ANI), which uses a light-weight DNN on the client-side to inject noise to each input, before transmitting it to the service provider to perform inference.
Adversarial Examples in Modern Machine Learning: A Review
Recent research has found that many families of machine learning models are vulnerable to adversarial examples: inputs that are specifically designed to cause the target model to produce erroneous outputs.
Retrieving Signals in the Frequency Domain with Deep Complex Extractors
Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.
Retrieving Signals with Deep Complex Extractors
Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.
Bayesian Model-Agnostic Meta-Learning
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem.
