1 code implementation • 30 Mar 2024 • Ruyang Liu, Chen Li, Haoran Tang, Yixiao Ge, Ying Shan, Ge Li
In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs?
no code implementations • 27 Feb 2024 • Jingying Wang, Haoran Tang, Taylor Kantor, Tandis Soltani, Vitaliy Popov, Xu Wang
The segmentation pipeline enables functionalities to create visual questions and feedback desired by surgeons from a formative study.
1 code implementation • 12 Dec 2023 • Xiang Li, Haoran Tang, Siyu Chen, Ziwei Wang, Anurag Maravi, Marcin Abram
In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclusions when tasked with answering complex questions.
no code implementations • 5 Dec 2023 • Haoran Tang, Xin Zhou, Jieren Deng, Zhihong Pan, Hao Tian, Pratik Chaudhari
Newly developed diffusion-based techniques have showcased phenomenal abilities in producing a wide range of high-quality images, sparking considerable interest in various applications.
no code implementations • 20 Oct 2023 • Zixuan Wang, Haoran Tang, Haibo Wang, Bo Qin, Mark D. Butala, Weiming Shen, Hongwei Wang
Despite the remarkable results that can be achieved by data-driven intelligent fault diagnosis techniques, they presuppose the same distribution of training and test data as well as sufficient labeled data.
no code implementations • ICCV 2023 • Yuanyi Zhong, Haoran Tang, Jun-Kun Chen, Yu-Xiong Wang
Though self-supervised contrastive learning (CL) has shown its potential to achieve state-of-the-art accuracy without any supervision, its behavior still remains under investigated by academia.
no code implementations • 29 Nov 2022 • Lukas Zhornyak, Zhengjie Xu, Haoran Tang, Jianbo Shi
We present HashEncoding, a novel autoencoding architecture that leverages a non-parametric multiscale coordinate hash function to facilitate a per-pixel decoder without convolutions.
no code implementations • 10 Nov 2022 • Zeqian Li, Keyu Qiu, Chenxu Jiao, Wen Zhu, Haoran Tang
This paper describes a French dialect recognition system that will appropriately distinguish between different regional French dialects.
no code implementations • 10 Jun 2022 • Yuanyi Zhong, Haoran Tang, Junkun Chen, Jian Peng, Yu-Xiong Wang
Our insight has implications in improving the downstream robustness of supervised learning.
no code implementations • 28 Jan 2022 • Changwei Xu, Jianfei Yang, Haoran Tang, Han Zou, Cheng Lu, Tianshuo Zhang
Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models.
no code implementations • 23 Sep 2019 • Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, Sergey Levine
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 8 Nov 2018 • Dennis Lee, Haoran Tang, Jeffrey O. Zhang, Huazhe Xu, Trevor Darrell, Pieter Abbeel
We present a novel modular architecture for StarCraft II AI.
3 code implementations • ICML 2017 • Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine
We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before.
3 code implementations • NeurIPS 2017 • Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.
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