Search Results for author: Tomas Pfister

Found 53 papers, 20 papers with code

Neural Spline Search for Quantile Probabilistic Modeling

no code implementations12 Jan 2023 Ruoxi Sun, Chun-Liang Li, Sercan O. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister

Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility.

regression Time Series Forecasting

QueryForm: A Simple Zero-shot Form Entity Query Framework

no code implementations14 Nov 2022 Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.

Transfer Learning

Test-Time Adaptation for Visual Document Understanding

no code implementations15 Jun 2022 Sayna Ebrahimi, Sercan O. Arik, Tomas Pfister

Self-supervised pretraining has been able to produce transferable representations for various visual document understanding (VDU) tasks.

Language Modelling Question Answering +2

Invariant Structure Learning for Better Generalization and Causal Explainability

no code implementations13 Jun 2022 Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister

ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint.

Self-Supervised Learning

Interpretable Mixture of Experts for Structured Data

no code implementations5 Jun 2022 Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister

We introduce a novel framework, Interpretable Mixture of Experts (IME), that provides interpretability for structured data while preserving accuracy.

Prefix Conditioning Unifies Language and Label Supervision

no code implementations2 Jun 2022 Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, Tomas Pfister

However, a naive unification of the real caption and the prompt sentences could lead to a complication in learning, as the distribution shift in text may not be handled properly in the language encoder.

Contrastive Learning

Data-Efficient and Interpretable Tabular Anomaly Detection

no code implementations3 Mar 2022 Chun-Hao Chang, Jinsung Yoon, Sercan Arik, Madeleine Udell, Tomas Pfister

In addition, the proposed framework, DIAD, can incorporate a small amount of labeled data to further boost anomaly detection performances in semi-supervised settings.

Additive models Anomaly Detection

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

no code implementations4 Feb 2022 Sercan O. Arik, Nathanael C. Yoder, Tomas Pfister

Real-world time-series datasets often violate the assumptions of standard supervised learning for forecasting -- their distributions evolve over time, rendering the conventional training and model selection procedures suboptimal.

Model Selection Self-Supervised Learning +1

Decoupling Local and Global Representations of Time Series

1 code implementation4 Feb 2022 Sana Tonekaboni, Chun-Liang Li, Sercan Arik, Anna Goldenberg, Tomas Pfister

Learning representations that capture the factors contributing to this variability enables a better understanding of the data via its underlying generative process and improves performance on downstream machine learning tasks.

Time Series

Towards Group Robustness in the presence of Partial Group Labels

no code implementations10 Jan 2022 Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister

Such a requirement is impractical in situations where the data labeling efforts for minority or rare groups are significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.

Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types

no code implementations21 Dec 2021 Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister

We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings.

Anomaly Detection Deep Clustering +1

Learning to Prompt for Continual Learning

1 code implementation CVPR 2022 Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister

The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.

Continual Learning Image Classification

Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning

no code implementations29 Sep 2021 Justin Lazarow, Kihyuk Sohn, Chun-Liang Li, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister

While remarkable progress in imbalanced supervised learning has been made recently, less attention has been given to the setting of imbalanced semi-supervised learning (SSL) where not only is a few labeled data provided, but the underlying data distribution can be severely imbalanced.

Pseudo Label

Invariant Learning with Partial Group Labels

no code implementations29 Sep 2021 Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister

Such a requirement is impractical in situations where the data labelling efforts for minority or rare groups is significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.

Learning Fast Sample Re-weighting Without Reward Data

1 code implementation ICCV 2021 Zizhao Zhang, Tomas Pfister

Training sample re-weighting is an effective approach for tackling data biases such as imbalanced and corrupted labels.

Meta-Learning

Controlling Neural Networks with Rule Representations

1 code implementation NeurIPS 2021 Sungyong Seo, Sercan O. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister

The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio.

Decision Making

Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding

5 code implementations26 May 2021 Zizhao Zhang, Han Zhang, Long Zhao, Ting Chen, Sercan O. Arik, Tomas Pfister

Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well.

Image Classification Image Generation

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

2 code implementations CVPR 2021 Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister

We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data.

Anomaly Detection Data Augmentation +4

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

no code implementations11 Jan 2021 Kunpeng Li, Zizhao Zhang, Guanhang Wu, Xuehan Xiong, Chen-Yu Lee, Zhichao Lu, Yun Fu, Tomas Pfister

To address this issue, we introduce a new method for pre-training video action recognition models using queried web videos.

Action Recognition Pseudo Label

Exploring Sub-Pseudo Labels for Learning from Weakly-Labeled Web Videos

no code implementations1 Jan 2021 Kunpeng Li, Zizhao Zhang, Guanhang Wu, Xuehan Xiong, Chen-Yu Lee, Yun Fu, Tomas Pfister

To address this issue, we introduce a new method for pre-training video action recognition models using queried web videos.

Action Recognition Pseudo Label

Differentiable Top-k with Optimal Transport

no code implementations NeurIPS 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

Finding the k largest or smallest elements from a collection of scores, i. e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining.

Information Retrieval Retrieval

Differentiable Top-$k$ with Optimal Transport

no code implementations NeurIPS Workshop LMCA 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The top-$k$ operation, i. e., finding the $k$ largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.

Information Retrieval Retrieval

A Simple Semi-Supervised Learning Framework for Object Detection

5 code implementations10 May 2020 Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, Tomas Pfister

Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data.

Ranked #10 on Semi-Supervised Object Detection on COCO 100% labeled data (using extra training data)

Data Augmentation Image Classification +3

Differentiable Top-k Operator with Optimal Transport

no code implementations16 Feb 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The top-k operation, i. e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.

Information Retrieval Retrieval

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

31 code implementations19 Dec 2019 Bryan Lim, Sercan O. Arik, Nicolas Loeff, Tomas Pfister

Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i. e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target.

Interpretable Machine Learning Time Series Forecasting

Distance-Based Learning from Errors for Confidence Calibration

no code implementations ICLR 2020 Chen Xing, Sercan Arik, Zizhao Zhang, Tomas Pfister

To circumvent this by inferring the distance for every test sample, we propose to train a confidence model jointly with the classification model.

Classification General Classification

On Completeness-aware Concept-Based Explanations in Deep Neural Networks

2 code implementations NeurIPS 2020 Chih-Kuan Yeh, Been Kim, Sercan O. Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar

Next, we propose a concept discovery method that aims to infer a complete set of concepts that are additionally encouraged to be interpretable, which addresses the limitations of existing methods on concept explanations.

Generative Modeling for Small-Data Object Detection

1 code implementation ICCV 2019 Lanlan Liu, Michael Muelly, Jia Deng, Tomas Pfister, Li-Jia Li

This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense.

object-detection Object Detection +2

Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost

no code implementations ECCV 2020 Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan O. Arik, Larry S. Davis, Tomas Pfister

Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance.

Active Learning Image Classification +1

Distilling Effective Supervision from Severe Label Noise

2 code implementations CVPR 2020 Zizhao Zhang, Han Zhang, Sercan O. Arik, Honglak Lee, Tomas Pfister

For instance, on CIFAR100 with a $40\%$ uniform noise ratio and only 10 trusted labeled data per class, our method achieves $80. 2{\pm}0. 3\%$ classification accuracy, where the error rate is only $1. 4\%$ higher than a neural network trained without label noise.

Image Classification

LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling

1 code implementation26 Sep 2019 Jinsung Yoon, Sercan O. Arik, Tomas Pfister

Understanding black-box machine learning models is crucial for their widespread adoption.

reinforcement Learning

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

no code implementations25 Sep 2019 Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan O. Arik, Larry S. Davis, Tomas Pfister

Active learning (AL) aims to integrate data labeling and model training in a unified way, and to minimize the labeling budget by prioritizing the selection of high value data that can best improve model performance.

Active Learning Representation Learning

On Concept-Based Explanations in Deep Neural Networks

no code implementations25 Sep 2019 Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister

Next, we propose a concept discovery method that considers two additional constraints to encourage the interpretability of the discovered concepts.

Data Valuation using Reinforcement Learning

1 code implementation ICML 2020 Jinsung Yoon, Sercan O. Arik, Tomas Pfister

To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL).

Domain Adaptation Meta-Learning +2

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

no code implementations20 Sep 2019 Yucen Luo, Jun Zhu, Tomas Pfister

Recently deep neural networks have shown their capacity to memorize training data, even with noisy labels, which hurts generalization performance.

Learning with noisy labels

Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning

no code implementations ECCV 2020 Linchao Zhu, Sercan O. Arik, Yi Yang, Tomas Pfister

We propose a novel adaptive transfer learning framework, learning to transfer learn (L2TL), to improve performance on a target dataset by careful extraction of the related information from a source dataset.

reinforcement-learning reinforcement Learning +1

TabNet: Attentive Interpretable Tabular Learning

18 code implementations20 Aug 2019 Sercan O. Arik, Tomas Pfister

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet.

Decision Making Poker Hand Classification +2

Inserting Videos into Videos

no code implementations CVPR 2019 Donghoon Lee, Tomas Pfister, Ming-Hsuan Yang

To synthesize a realistic video, the network renders each frame based on the current input and previous frames.

Object Tracking Person Re-Identification

Harmonic Unpaired Image-to-image Translation

no code implementations ICLR 2019 Rui Zhang, Tomas Pfister, Jia Li

The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations.

Image-to-Image Translation Translation

ProtoAttend: Attention-Based Prototypical Learning

4 code implementations17 Feb 2019 Sercan O. Arik, Tomas Pfister

We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes.

Decision Making General Classification +1

Learning from Simulated and Unsupervised Images through Adversarial Training

9 code implementations CVPR 2017 Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russ Webb

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.

Ranked #3 on Image-to-Image Translation on Cityscapes Labels-to-Photo (Per-class Accuracy metric)

Domain Adaptation Gaze Estimation +2

Personalizing Human Video Pose Estimation

no code implementations CVPR 2016 James Charles, Tomas Pfister, Derek Magee, David Hogg, Andrew Zisserman

The outcome is a substantial improvement in the pose estimates for the target video using the personalized ConvNet compared to the original generic ConvNet.

Optical Flow Estimation Pose Estimation

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