Search Results for author:
Found 27 papers, 11 papers with code
Uniform Inference in High-Dimensional Threshold Regression Models
We develop uniform inference for high-dimensional threshold regression parameters and valid inference for the threshold parameter in this paper.
Terraced Compression Method with Automated Threshold Selection for Multidimensional Image Clustering of Heterogeneous Bodies
The results illustrate the method's efficacy in detecting and identifying various heterogeneous body types, depths, and thicknesses.
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.
Large Language Models are In-Context Molecule Learners
Specifically, ICMA incorporates the following three stages: Hybrid Context Retrieval, Post-retrieval Re-ranking, and In-context Molecule Tuning.
ChemLLM: A Chemical Large Language Model
However, the community lacks an LLM specifically designed for chemistry.
Fast Graph Condensation with Structure-based Neural Tangent Kernel
The rapid development of Internet technology has given rise to a vast amount of graph-structured data.
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests.
ALWOD: Active Learning for Weakly-Supervised Object Detection
In this work, we propose ALWOD, a new framework that addresses this problem by fusing active learning (AL) with weakly and semi-supervised object detection paradigms.
Beyond Static Datasets: A Deep Interaction Approach to LLM Evaluation
Existing LLM evaluation methods are mainly supervised signal-based which depends on static datasets and cannot evaluate the ability of LLMs in dynamic real-world scenarios where deep interaction widely exists.
Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm
However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services.
Recommender Systems in the Era of Large Language Models (LLMs)
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.
Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective
In this work, we propose a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning.
Ranked #3 on
Text-based de novo Molecule Generation
on ChEBI-20
Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing
Despite increasing interest in the automatic detection of media frames in NLP, the problem is typically simplified as single-label classification and adopts a topic-like view on frames, evading modelling the broader document-level narrative.
Generative Diffusion Models on Graphs: Methods and Applications
Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.
Improving Chinese Named Entity Recognition by Search Engine Augmentation
In this paper, we propose a neural-based approach to perform semantic augmentation using external knowledge from search engine for Chinese NER.
Chinese Named Entity Recognition
named-entity-recognition
+2
ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing
This paper studies the problem of learning the shape given in the form of point clouds by inverse sketch-and-extrude.
Exploring Effective Information Utilization in Multi-Turn Topic-Driven Conversations
In order to expand the information that PLMs can utilize, we encode topic and dialogue history information using certain prompts with multiple channels of Fusion-in-Decoder (FiD) and explore the influence of three different channel settings.
MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition
Some Chinese characters are quite similar as they share the same components or have similar pronunciations.
Chinese Named Entity Recognition
named-entity-recognition
+2
CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem.
Understanding and Improving Early Stopping for Learning with Noisy Labels
Instead of the early stopping, which trains a whole DNN all at once, we initially train former DNN layers by optimizing the DNN with a relatively large number of epochs.
Ranked #8 on
Learning with noisy labels
on CIFAR-10N-Aggregate
Algorithmic subsampling under multiway clustering
This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster dependent data.
CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval
Despite the abundance of multi-modal data, such as image-text pairs, there has been little effort in understanding the individual entities and their different roles in the construction of these data instances.
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
The traditional transition matrix is limited to model closed-set label noise, where noisy training data has true class labels within the noisy label set.
Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images
Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems.
Deep Cooking: Predicting Relative Food Ingredient Amounts from Images
In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients.
The Strength of the Weakest Supervision: Topic Classification Using Class Labels
When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels.
Adaptive Feature Representation for Visual Tracking
Robust feature representation plays significant role in visual tracking.
