Sercan Ö. Arık

RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling

2019 · arXiv (Cornell University)

Understanding black-box machine learning models is important towards their widespread adoption. However, developing globally interpretable models that explain the behavior of the entire model is challenging. An alternative approach is to explain black-box models through …

Data-Efficient and Interpretable Tabular Anomaly Detection

2022 · arXiv (Cornell University)

Anomaly detection (AD) plays an important role in numerous applications. We focus on two understudied aspects of AD that are critical for integration into real-world applications. First, most AD methods cannot incorporate labeled data that …

Better Zero-Shot Reasoning with Self-Adaptive Prompting

2023 · arXiv (Cornell University)

Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible by their strong few and zero-shot abilities -- they can effectively learn …

Deep Voice: Real-time Neural Text-to-Speech

2017 · arXiv (Cornell University)

We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model …

Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence\n Learning

2017 · arXiv (Cornell University)

We present Deep Voice 3, a fully-convolutional attention-based neural\ntext-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural\nspeech synthesis systems in naturalness while training ten times faster. We\nscale Deep Voice 3 to data set sizes unprecedented …