3 citations · 3 across the 6 of their papers we have counts for
6 papers
Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets?
Nigel Bastian Cendra, Abdelhamid Ezzerg, Fernando Julio Cendra +2
Gradient-free post-training has emerged as a compelling alternative to gradient-based optimization for large language models (LLMs), but existing approaches remain costly. We ask w…
PartCo: Part-Level Correspondence Priors Enhance Category Discovery
Fernando Julio Cendra, Kai Han
Generalized Category Discovery (GCD) aims to identify both known and novel categories within unlabeled data by leveraging a set of labeled examples from known categories. Existing…
ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models
Fernando Julio Cendra, Kai Han
The inherent ambiguity in defining visual concepts poses significant challenges for modern generative models, such as the diffusion-based Text-to-Image (T2I) models, in accurately…
Effective Prompt Pool Learning for Continual Category Discovery
Fernando Julio Cendra, Xinghui Li, Kai Han
This paper studies effective prompt pool learning for Continual Category Discovery (CCD), a challenging open-world setting where a model must discover novel categories from a conti…
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning
Tsai Hor Chan, Fernando Julio Cendra, Lan Ma +2
Graph-based methods have been extensively applied to whole-slide histopathology image (WSI) analysis due to the advantage of modeling the spatial relationships among different enti…
SL3D: Self-supervised-Self-labeled 3D Recognition
Fernando Julio Cendra, Lan Ma, Jiajun Shen +1
Deep learning has attained remarkable success in many 3D visual recognition tasks, including shape classification, object detection, and semantic segmentation. However, many of the…