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08/25/2023
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series
Real-world time series data that commonly reflect sequential human behav...
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08/24/2023
Business Metric-Aware Forecasting for Inventory Management
Time-series forecasts play a critical role in business planning. However...
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05/26/2023
LANISTR: Multimodal Learning from Structured and Unstructured Data
Multimodal large-scale pretraining has shown impressive performance gain...
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04/06/2023
SLM: End-to-end Feature Selection via Sparse Learnable Masks
Feature selection has been widely used to alleviate compute requirements...
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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...
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11/30/2022
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Semi-supervised anomaly detection is a common problem, as often the data...
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11/12/2022
Provable Membership Inference Privacy
In applications involving sensitive data, such as finance and healthcare...
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09/16/2022
Self-Supervised Learning with an Information Maximization Criterion
Self-supervised learning allows AI systems to learn effective representa...
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06/15/2022
Test-Time Adaptation for Visual Document Understanding
Self-supervised pretraining has been able to produce transferable repres...
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06/13/2022
Invariant Structure Learning for Better Generalization and Causal Explainability
Learning the causal structure behind data is invaluable for improving ge...
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06/05/2022
Interpretable Mixture of Experts for Structured Data
With the growth of machine learning for structured data, the need for re...
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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...
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06/14/2021
Controlling Neural Networks with Rule Representations
We propose a novel training method to integrate rules into deep learning...
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06/11/2021
Self-Trained One-class Classification for Unsupervised Anomaly Detection
Anomaly detection (AD), separating anomalies from normal data, has vario...
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08/03/2020
Interpretable Sequence Learning for COVID-19 Forecasting
We propose a novel approach that integrates machine learning into compar...
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07/15/2020
Explaining Deep Neural Networks using Unsupervised Clustering
We propose a novel method to explain trained deep neural networks (DNNs)...
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12/19/2019
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
Multi-horizon forecasting problems often contain a complex mix of inputs...
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10/17/2019
On Concept-Based Explanations in Deep Neural Networks
Deep neural networks (DNNs) build high-level intelligence on low-level r...
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10/16/2019
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost
Active learning (AL) integrates data labeling and model training to mini...
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10/01/2019
IEG: Robust Neural Network Training to Tackle Severe Label Noise
Collecting large-scale data with clean labels for supervised training of...
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09/26/2019
RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling
Understanding black-box machine learning models is important towards the...
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09/25/2019
Data Valuation using Reinforcement Learning
Quantifying the value of data is a fundamental problem in machine learni...
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08/29/2019
Learning to Transfer Learn
We propose a novel framework, learning to transfer learn (L2TL), to impr...
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08/20/2019
TabNet: Attentive Interpretable Tabular Learning
We propose a novel high-performance interpretable deep tabular data lear...
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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...
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08/20/2018
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks
We propose the multi-head convolutional neural network (MCNN) architectu...
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06/12/2018
Resource-Efficient Neural Architect
Neural Architecture Search (NAS) is a laborious process. Prior work on a...
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02/14/2018
Neural Voice Cloning with a Few Samples
Voice cloning is a highly desired feature for personalized speech interf...
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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...
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10/20/2017
Deep Voice 3: 2000-Speaker Neural Text-to-Speech
We present Deep Voice 3, a fully-convolutional attention-based neural te...
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03/15/2017
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Keyword spotting (KWS) constitutes a major component of human-technology...
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02/25/2017
