Search Results for author: Tianle Chen

Found 10 papers, 3 papers with code

Knowledge Graph Large Language Model (KG-LLM) for Link Prediction

no code implementations12 Mar 2024 Dong Shu, Tianle Chen, Mingyu Jin, Yiting Zhang, Mengnan Du, Yongfeng Zhang

The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques.

In-Context Learning Knowledge Graphs +3

Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

1 code implementation23 Oct 2023 Tai-Yu Pan, Chenyang Ma, Tianle Chen, Cheng Perng Phoo, Katie Z Luo, Yurong You, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao

Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.

3D Object Detection Colorization +4

Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation

1 code implementation9 May 2023 Tianle Chen, Zheda Mai, Ruiwen Li, Wei-Lun Chao

Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious pixel-level annotation by using only image-level annotation.

Object Pseudo Label +2

Learning with Free Object Segments for Long-Tailed Instance Segmentation

no code implementations22 Feb 2022 Cheng Zhang, Tai-Yu Pan, Tianle Chen, Jike Zhong, WenJin Fu, Wei-Lun Chao

One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects.

Instance Segmentation Object +1

Learning to Generate the Unknowns for Open-set Domain Adaptation

no code implementations1 Jan 2021 Mahsa Baktashmotlagh, Tianle Chen, Mathieu Salzmann

In this setting, existing techniques focus on the challenging task of isolating the unknown target samples, so as to avoid the negative transfer resulting from aligning the source feature distributions with the broader target one that encompasses the additional unknown classes.

Domain Adaptation

Defending Adversarial Attacks via Semantic Feature Manipulation

no code implementations3 Feb 2020 Shuo Wang, Tianle Chen, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen

In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner.

General Classification

OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning

no code implementations18 Jan 2020 Shuo Wang, Tianle Chen, Shangyu Chen, Carsten Rudolph, Surya Nepal, Marthie Grobler

Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency.

Anomaly Detection Disentanglement

Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models

no code implementations10 Jan 2020 Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen

In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}.

Electrocardiography (ECG) Electroencephalogram (EEG) +3

Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecasting

1 code implementation29 Dec 2018 Tianle Chen, Brian Keng, Javier Moreno

However, buyer purchase patterns are extremely diverse and sparse on a per-product level due to population heterogeneity as well as dependence in purchase patterns across product categories.

Marketing Survival Analysis

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