Sercan O. Arik | DeepAI

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∙ 03/10/2023

TSMixer: An all-MLP Architecture for Time Series Forecasting

Real-world time-series datasets are often multivariate with complex dyna...

0 Si-An Chen, et al. ∙

∙ 11/30/2022

SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch

Semi-supervised anomaly detection is a common problem, as often the data...

0 Jinsung Yoon, et al. ∙

∙ 11/12/2022

Provable Membership Inference Privacy

In applications involving sensitive data, such as finance and healthcare...

0 Zachary Izzo, et al. ∙

∙ 09/16/2022

Self-Supervised Learning with an Information Maximization Criterion

Self-supervised learning allows AI systems to learn effective representa...

0 Serdar Ozsoy, et al. ∙

∙ 06/15/2022

Test-Time Adaptation for Visual Document Understanding

Self-supervised pretraining has been able to produce transferable repres...

0 Sayna Ebrahimi, et al. ∙

∙ 06/13/2022

Invariant Structure Learning for Better Generalization and Causal Explainability

Learning the causal structure behind data is invaluable for improving ge...

0 Yunhao Ge, et al. ∙

∙ 06/05/2022

Interpretable Mixture of Experts for Structured Data

With the growth of machine learning for structured data, the need for re...

16 Aya Abdelsalam Ismail, et al. ∙

∙ 02/04/2022

Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series

Real-world time-series datasets often violate the assumptions of standar...

0 Sercan O. Arik, et al. ∙

∙ 06/14/2021

Controlling Neural Networks with Rule Representations

We propose a novel training method to integrate rules into deep learning...

0 Sungyong Seo, et al. ∙

∙ 06/11/2021

Self-Trained One-class Classification for Unsupervised Anomaly Detection

Anomaly detection (AD), separating anomalies from normal data, has vario...

0 Jinsung Yoon, et al. ∙

∙ 08/03/2020

Interpretable Sequence Learning for COVID-19 Forecasting

We propose a novel approach that integrates machine learning into compar...

36 Sercan O. Arik, et al. ∙

∙ 07/15/2020

Explaining Deep Neural Networks using Unsupervised Clustering

We propose a novel method to explain trained deep neural networks (DNNs)...

144 Yu-han Liu, et al. ∙

∙ 12/19/2019

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

Multi-horizon forecasting problems often contain a complex mix of inputs...

19 Bryan Lim, et al. ∙

∙ 10/17/2019

On Concept-Based Explanations in Deep Neural Networks

Deep neural networks (DNNs) build high-level intelligence on low-level r...

21 Chih-Kuan Yeh, et al. ∙

∙ 10/16/2019

Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost

Active learning (AL) integrates data labeling and model training to mini...

28 Mingfei Gao, et al. ∙

∙ 10/01/2019

IEG: Robust Neural Network Training to Tackle Severe Label Noise

Collecting large-scale data with clean labels for supervised training of...

10 Zizhao Zhang, et al. ∙

∙ 09/26/2019

RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling

Understanding black-box machine learning models is important towards the...

20 Jinsung Yoon, et al. ∙

∙ 09/25/2019

Data Valuation using Reinforcement Learning

Quantifying the value of data is a fundamental problem in machine learni...

11 Jinsung Yoon, et al. ∙

∙ 08/29/2019

Learning to Transfer Learn

We propose a novel framework, learning to transfer learn (L2TL), to impr...

10 Linchao Zhu, et al. ∙

∙ 08/20/2019

TabNet: Attentive Interpretable Tabular Learning

We propose a novel high-performance interpretable deep tabular data lear...

3 Sercan O. Arik, et al. ∙

∙ 02/17/2019

Attention-Based Prototypical Learning Towards Interpretable, Confident and Robust Deep Neural Networks

We propose a new framework for prototypical learning that bases decision...

4 Sercan O. Arik, et al. ∙

∙ 08/20/2018

Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks

We propose the multi-head convolutional neural network (MCNN) architectu...

0 Sercan O. Arik, et al. ∙

∙ 06/12/2018

Resource-Efficient Neural Architect

Neural Architecture Search (NAS) is a laborious process. Prior work on a...

0 Yanqi Zhou, et al. ∙

∙ 02/14/2018

Neural Voice Cloning with a Few Samples

Voice cloning is a highly desired feature for personalized speech interf...

0 Sercan O. Arik, et al. ∙

∙ 10/20/2017

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

We present Deep Voice 3, a fully-convolutional attention-based neural te...

0 Wei Ping, et al. ∙

∙ 10/20/2017

Deep Voice 3: 2000-Speaker Neural Text-to-Speech

We present Deep Voice 3, a fully-convolutional attention-based neural te...

0 Wei Ping, et al. ∙

∙ 03/15/2017

Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting

Keyword spotting (KWS) constitutes a major component of human-technology...

0 Sercan O. Arik, et al. ∙

∙ 02/25/2017

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

We present Deep Voice, a production-quality text-to-speech system constr...

0 Sercan O. Arik, et al. ∙