Pfister, Tomas

Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021) · International Journal of Forecasting

Time Series Analysis and ForecastingForecasting Techniques and Applications

Cited by 3,291

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

Li, C.-L., Sohn, K., Yoon, J., & Pfister, T. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Industrial Vision Systems and Defect DetectionAnomaly Detection Techniques and Applications

Cited by 1,107

Recognising spontaneous facial micro-expressions

Pfister, T., Li, X., Zhao, G., & Pietikainen, M. (2011) · 2011 International Conference on Computer Vision

Emotion and Mood RecognitionFace recognition and analysis

Cited by 450

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

Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 316

ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory

Ouyang, S., Yan, J., Hsu, I.-H., Chen, Y., Jiang, K., Wang, Z., Han, R., Le, L. T., Daruki, S., Tang, X., et al. (2025) · arXiv

AI-based Problem Solving and PlanningDomain Adaptation and Few-Shot Learning

Cited by 295

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

Saito, K., Sohn, K., Zhang, X., Li, C.-L., Lee, C.-Y., Saenko, K., & Pfister, T. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Image Retrieval and Classification TechniquesMultimodal Machine Learning Applications

Cited by 145

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Cao, D., Jia, F., Arik, S. O., Pfister, T., Zheng, Y., Ye, W., & Liu, Y. (2023) · arXiv

Time Series Analysis and ForecastingForecasting Techniques and Applications

Cited by 59

Chain of Agents: Large Language Models Collaborating on Long-Context Tasks

Zhang, Y., Sun, R., Chen, Y., Pfister, T., Zhang, R., & Arik, S. Ö. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 47

Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Hsieh, C.-Y., Chuang, Y.-S., Li, C.-L., Wang, Z., Le, L., Kumar, A., Glass, J., Ratner, A., Lee, C.-Y., Krishna, R., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingNatural Language Processing Techniques

Cited by 31

SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)

Sun, R., Arik, S. Ö., Muzio, A., Miculicich, L., Gundabathula, S., Yin, P., Dai, H., Nakhost, H., Sinha, R., Wang, Z., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 31

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

Feng, S., Wang, Z., Wang, Y., Ebrahimi, S., Palangi, H., Miculicich, L., Kulshrestha, A., Rauschmayr, N., Choi, Y., Tsvetkov, Y., et al. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 24

Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems

Feng, S., Wang, Z., Goyal, P., Wang, Y., Shi, W., Xia, H., Palangi, H., Zettlemoyer, L., Tsvetkov, Y., Lee, C.-Y., et al. (2025) · arXiv

Topic ModelingMetaheuristic Optimization Algorithms Research

Cited by 18

Prefix Conditioning Unifies Language and Label Supervision

Saito, K., Sohn, K., Zhang, X., Li, C.-L., Lee, C.-Y., Saenko, K., & Pfister, T. (2022) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 17

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

Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 16

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents

Tan, Z., Yan, J., Hsu, I.-H., Han, R., Wang, Z., Le, L., Song, Y., Chen, Y., Palangi, H., Lee, G., et al. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Speech and dialogue systemsAI-based Problem Solving and Planning

Cited by 16

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs

Chen, J., Yoon, J., Ebrahimi, S., Arik, S., Pfister, T., & Jha, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 13

Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting

Wang, Z., Wang, Z., Le, L., Zheng, H. S., Mishra, S., Perot, V., Zhang, Y., Mattapalli, A., Taly, A., Shang, J., et al. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 9

VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation

Miculicich, L., Parmar, M., Palangi, H., Dvijotham, K. D., Montanari, M., Pfister, T., & Le, L. T. (2025) · arXiv

Formal Methods in VerificationSafety Systems Engineering in Autonomy

Cited by 2

SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging

Pourreza, M., Sun, R., Li, H., Miculicich, L., Pfister, T., & Arik, S. O. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Science

Salemi, A., Parmar, M., Goyal, P., Song, Y., Yoon, J., Zamani, H., Pfister, T., & Palangi, H. (2025) · arXiv

Multi-Agent Systems and NegotiationInformation Retrieval and Search Behavior

Cited by 2

Budget-Aware Tool-Use Enables Effective Agent Scaling

Liu, T., Wang, Z., Miao, J., Hsu, I.-H., Yan, J., Chen, J., Han, R., Xu, F., Chen, Y., Jiang, K., et al. (2025) · arXiv

Information Retrieval and Search BehaviorAI-based Problem Solving and Planning

Cited by 1

Harness^2: Recursive Agent Harnessing for an Open World

Xu, R., Chen, Y., CuiZhu, Z., Mishra, B. D., Ming, Y., Yu, H., Han, R., Lee, C.-Y., & Pfister, T. (2026) · arXiv

AI-based Problem Solving and PlanningEvolutionary Algorithms and Applications

Cited by 1

Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Hsieh, C.-Y., Chuang, Y.-S., Li, C.-L., Wang, Z., Le, L. T., Kumar, A., Glass, J., Ratner, A., Lee, C.-Y., Krishna, R., et al. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 0

Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval

Chen, Y., Yoon, J., Sachan, D. S., Wang, Q., Cohen-Addad, V., Bateni, M., Lee, C.-Y., & Pfister, T. (2024) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 0