b'Tomas Pfister'

research

∙ 08/25/2023

PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series

Real-world time series data that commonly reflect sequential human behav...

0 Nicasia Beebe-Wang, et al. ∙

research

∙ 08/01/2023

Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models

Today, large language models (LLMs) are taught to use new tools by provi...

0 Cheng-Yu Hsieh, et al. ∙

research

∙ 05/26/2023

LANISTR: Multimodal Learning from Structured and Unstructured Data

Multimodal large-scale pretraining has shown impressive performance gain...

0 Sayna Ebrahimi, et al. ∙

research

∙ 05/03/2023

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Deploying large language models (LLMs) is challenging because they are m...

0 Cheng-Yu Hsieh, et al. ∙

research

∙ 04/07/2023

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction

Selective prediction aims to learn a reliable model that abstains from m...

2 Jiefeng Chen, et al. ∙

research

∙ 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. ∙

research

∙ 02/06/2023

Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval

In Composed Image Retrieval (CIR), a user combines a query image with te...

0 Kuniaki Saito, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 06/02/2022

Prefix Conditioning Unifies Language and Label Supervision

Vision-language contrastive learning suggests a new learning paradigm by...

0 Kuniaki Saito, et al. ∙

research

∙ 03/30/2022

Learning Instance-Specific Adaptation for Cross-Domain Segmentation

We propose a test-time adaptation method for cross-domain image segmenta...

4 Yuliang Zou, et al. ∙

research

∙ 03/16/2022

FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction

Sequence modeling has demonstrated state-of-the-art performance on natur...

0 Chen-Yu Lee, et al. ∙

research

∙ 03/03/2022

Data-Efficient and Interpretable Tabular Anomaly Detection

Anomaly detection (AD) plays an important role in numerous applications....

0 Chun-Hao Chang, et al. ∙

research

∙ 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. ∙

research

∙ 02/04/2022

Decoupling Local and Global Representations of Time Series

Real-world time series data are often generated from several sources of ...

0 Sana Tonekaboni, et al. ∙

research

∙ 01/10/2022

Towards Group Robustness in the presence of Partial Group Labels

Learning invariant representations is an important requirement when trai...

0 Vishnu Suresh Lokhande, et al. ∙

research

∙ 12/21/2021

Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types

We introduce anomaly clustering, whose goal is to group data into semant...

22 Kihyuk Sohn, et al. ∙

research

∙ 09/07/2021

Learning Fast Sample Re-weighting Without Reward Data

Training sample re-weighting is an effective approach for tackling data ...

1 Zizhao Zhang, et al. ∙

research

∙ 06/21/2021

ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction

Natural reading orders of words are crucial for information extraction f...

0 Chen-Yu Lee, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 05/26/2021

Aggregating Nested Transformers

Although hierarchical structures are popular in recent vision transforme...

9 Zizhao Zhang, et al. ∙

research

∙ 04/08/2021

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

We aim at constructing a high performance model for defect detection tha...

9 Chun-Liang Li, et al. ∙

research

∙ 01/11/2021

Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts

Learning visual knowledge from massive weakly-labeled web videos has att...

6 Kunpeng Li, et al. ∙

research

∙ 11/04/2020

Learning and Evaluating Representations for Deep One-class Classification

We present a two-stage framework for deep one-class classification. We f...

0 Kihyuk Sohn, et al. ∙

research

∙ 10/19/2020

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

Recent advances in semi-supervised learning (SSL) demonstrate that a com...

15 Yuliang Zou, et al. ∙

research

∙ 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. ∙

research

∙ 05/10/2020

A Simple Semi-Supervised Learning Framework for Object Detection

Semi-supervised learning (SSL) has promising potential for improving the...

3 Kihyuk Sohn, et al. ∙

research

∙ 02/16/2020

Differentiable Top-k Operator with Optimal Transport

The top-k operation, i.e., finding the k largest or smallest elements fr...

6 Yujia Xie, et al. ∙

research

∙ 12/19/2019

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

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

20 Bryan Lim, et al. ∙

research

∙ 12/03/2019

Distance-Based Learning from Errors for Confidence Calibration

Deep neural networks (DNNs) are poorly-calibrated when trained in conven...

0 Chen Xing, et al. ∙

research

∙ 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. ∙

research

∙ 10/16/2019

Generative Modeling for Small-Data Object Detection

This paper explores object detection in the small data regime, where onl...

18 Lanlan Liu, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 09/20/2019

A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels

Recently deep neural networks have shown their capacity to memorize trai...

0 Yucen Luo, et al. ∙

research

∙ 08/29/2019

Learning to Transfer Learn

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

10 Linchao Zhu, et al. ∙

research

∙ 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. ∙

research

∙ 03/15/2019

Inserting Videos into Videos

In this paper, we introduce a new problem of manipulating a given video ...

24 Donghoon Lee, et al. ∙

research

∙ 02/26/2019

Harmonic Unpaired Image-to-image Translation

The recent direction of unpaired image-to-image translation is on one ha...

4 Rui Zhang, et al. ∙

research

∙ 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. ∙

research

∙ 12/22/2016

Learning from Simulated and Unsupervised Images through Adversarial Training

With recent progress in graphics, it has become more tractable to train ...

0 Ashish Shrivastava, et al. ∙

research

∙ 11/20/2015

Personalizing Human Video Pose Estimation

We propose a personalized ConvNet pose estimator that automatically adap...

0 James Charles, et al. ∙

research

∙ 06/09/2015

Flowing ConvNets for Human Pose Estimation in Videos

The objective of this work is human pose estimation in videos, where mul...

0 Tomas Pfister, et al. ∙

Tomas Pfister

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