Search Results for author:
Found 9 papers, 6 papers with code
Using Natural Sentence Prompts for Understanding Biases in Language Models
Evaluation of biases in language models is often limited to synthetically generated datasets.
Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers
Experimental results on 4 datasets and BERT and distilBERT classifiers show that SP-Defense improves \r{ho} by 14. 6% and 13. 9% and decreases the attack success rate of SP-Attack by 30. 4% and 21. 2% on two classifiers respectively, and decreases the attack success rate of existing attack methods that involve multiple-word perturbations.
Making the End-User a Priority in Benchmarking: OrionBench for Unsupervised Time Series Anomaly Detection
The framework provides universal abstractions to represent models, extensibility to add new pipelines and datasets, hyperparameter standardization, pipeline verification, and frequent releases with published benchmarks.
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method.
Using Natural Sentences for Understanding Biases in Language Models
Evaluation of biases in language models is often limited to synthetically generated datasets.
Sintel: A Machine Learning Framework to Extract Insights from Signals
The detection of anomalies in time series data is a critical task with many monitoring applications.
Probabilistic Programming Bots in Intuitive Physics Game Play
Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the physics of objects.
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018.
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.
