no code implementations • 20 Nov 2024 • Gaurav Verma, Rachneet Kaur, Nishan Srishankar, Zhen Zeng, Tucker Balch, Manuela Veloso
Our experiments on two popular benchmarks -- Mind2Web & VisualWebArena -- show that using in-context demonstrations (for proprietary models) or meta-adaptation demonstrations (for meta-learned open-weights models) boosts task success rate by 3. 36% to 7. 21% over non-adapted state-of-the-art models, corresponding to a relative increase of 21. 03% to 65. 75%.
no code implementations • 19 Nov 2024 • Zhen Zeng, Leijiang Gu, Xun Yang, Zhangling Duan, Zenglin Shi, Meng Wang
MSCKE leverages a multimodal scope classifier that integrates both visual and textual information to accurately identify and update knowledge related to specific entities within images.
no code implementations • 9 Jul 2024 • Rachneet Kaur, Zhen Zeng, Tucker Balch, Manuela Veloso
Recent advancements in language modeling have shown promising results when applied to time series data.
no code implementations • 14 Jun 2024 • Tomas De la Rosa, Sriram Gopalakrishnan, Alberto Pozanco, Zhen Zeng, Daniel Borrajo
Travel planning is a complex task that involves generating a sequence of actions related to visiting places subject to constraints and maximizing some user satisfaction criteria.
no code implementations • 12 Jun 2024 • Kausik Lakkaraju, Rachneet Kaur, Zhen Zeng, Parisa Zehtabi, Sunandita Patra, Biplav Srivastava, Marco Valtorta
AI systems are notorious for their fragility; minor input changes can potentially cause major output swings.
no code implementations • 25 Apr 2024 • Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, Manuela Veloso, Svitlana Vyetrenko
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more.
no code implementations • 17 Mar 2024 • Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa, Tucker Balch, Manuela Veloso
Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges.
no code implementations • 17 Mar 2024 • Zhen Zeng, William Watson, Nicole Cho, Saba Rahimi, Shayleen Reynolds, Tucker Balch, Manuela Veloso
FlowMind further simplifies user interaction by presenting high-level descriptions of auto-generated workflows, enabling users to inspect and provide feedback effectively.
no code implementations • 11 Apr 2023 • Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa, Saba Rahimi, Tucker Balch, Manuela Veloso
In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods on forecasting intraday stock price change of S&P 500 constituents.
no code implementations • 15 Oct 2022 • Shanshan Wang, Zhen Zeng, Xun Yang, Xingyi Zhang
Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts.
no code implementations • 8 Aug 2022 • Huaizhen Tang, xulong Zhang, Jianzong Wang, Ning Cheng, Zhen Zeng, Edward Xiao, Jing Xiao
In this paper, a novel voice conversion framework, named $\boldsymbol T$ext $\boldsymbol G$uided $\boldsymbol A$utoVC(TGAVC), is proposed to more effectively separate content and timbre from speech, where an expected content embedding produced based on the text transcriptions is designed to guide the extraction of voice content.
1 code implementation • 5 Oct 2021 • Xiao Li, Yidong Du, Zhen Zeng, Odest Chadwicke Jenkins
This paper proposes a SEmantic understANding Network (SeanNet) architecture that enables an effective learning process with coupled visual and semantic inputs.
no code implementations • 2 Jul 2021 • Naftali Cohen, Srijan Sood, Zhen Zeng, Tucker Balch, Manuela Veloso
In this work, we address time-series forecasting as a computer vision task.
no code implementations • 24 Feb 2021 • Zhen Zeng, Tucker Balch, Manuela Veloso
In this paper, we propose to approach economic time series forecasting of multiple financial assets in a novel way via video prediction.
no code implementations • 3 Dec 2020 • Aolan Sun, Jianzong Wang, Ning Cheng, Huayi Peng, Zhen Zeng, Lingwei Kong, Jing Xiao
Graph-to-sequence model is proposed and formed by a graph encoder and an attentional decoder.
3 code implementations • 3 Dec 2020 • Zhen Zeng, Jianzong Wang, Ning Cheng, Jing Xiao
In this paper, an efficient network, named location-variable convolution, is proposed to model the dependencies of waveforms.
no code implementations • 18 Nov 2020 • Srijan Sood, Zhen Zeng, Naftali Cohen, Tucker Balch, Manuela Veloso
In this work, we leverage advances in deep learning to extend the field of time series forecasting to a visual setting.
1 code implementation • 16 Oct 2020 • Xiaotong Chen, Kaizhi Zheng, Zhen Zeng, Cameron Kisailus, Shreshtha Basu, James Cooney, Jana Pavlasek, Odest Chadwicke Jenkins
In this work, we combine the notions of affordance and category-level pose, and introduce the Affordance Coordinate Frame (ACF).
no code implementations • 13 Aug 2020 • Zhen Zeng, Jianzong Wang, Ning Cheng, Jing Xiao
Recent neural speech synthesis systems have gradually focused on the control of prosody to improve the quality of synthesized speech, but they rarely consider the variability of prosody and the correlation between prosody and semantics together.
no code implementations • 18 Jun 2020 • Zhen Zeng, Adrian Röfer, Odest Chadwicke Jenkins
SLiM simultaneously maintains the belief over a target object's location as well as landmark objects' locations, while accounting for probabilistic inter-object spatial relations.
2 code implementations • 4 Mar 2020 • Zhen Zeng, Jianzong Wang, Ning Cheng, Tian Xia, Jing Xiao
Targeting at both high efficiency and performance, we propose AlignTTS to predict the mel-spectrum in parallel.
no code implementations • 4 Mar 2020 • Aolan Sun, Jianzong Wang, Ning Cheng, Huayi Peng, Zhen Zeng, Jing Xiao
This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms.
no code implementations • 4 Oct 2019 • Zhen Zeng, Yuefeng Lu, Judong Shen, Wei Zheng, Peter Shaw, Mary Beth Dorr
Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system.
1 code implementation • 26 Oct 2018 • Zhen Zeng, Yunwen Zhou, Odest Chadwicke Jenkins, Karthik Desingh
Our results demonstrate that the particle filtering based inference of CT-Map provides improved object detection and pose estimation with respect to baseline methods that treat observations as independent samples of a scene.
Robotics
no code implementations • 3 Mar 2016 • Zhen Zeng, Benjamin Kuipers
We aim to enable robot to learn object manipulation by imitation.