Search Results for author:
Found 23 papers, 11 papers with code
Y Chromosome of Aisin Gioro, the Imperial House of Qing Dynasty
We therefore conclude that this haplotype is the Y chromosome of the House of Aisin Gioro.
Populations and Evolution
Cleaner Pretraining Corpus Curation with Neural Web Scraping
The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans.
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining.
ActiveRAG: Revealing the Treasures of Knowledge via Active Learning
Retrieval Augmented Generation (RAG) has introduced a new paradigm for Large Language Models (LLMs), aiding in the resolution of knowledge-intensive tasks.
Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval
To learn a unified embedding space for multi-modal retrieval, UniVL-DR proposes two techniques: 1) Universal embedding optimization strategy, which contrastively optimizes the embedding space using the modality-balanced hard negatives; 2) Image verbalization method, which bridges the modality gap between images and texts in the raw data space.
Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems.
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training.
INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair
INTERVENOR prompts Large Language Models (LLMs) to play distinct roles during the code repair process, functioning as both a Code Learner and a Code Teacher.
Ranked #14 on
Code Generation
on HumanEval
TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields
Place recognition plays an essential role in the field of autonomous driving and robot navigation.
Ranked #4 on
3D Place Recognition
on CS-Campus3D
MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
To facilitate the multi-modal retrieval tasks, we build the ClueWeb22-MM dataset based on the ClueWeb22 dataset, which regards anchor texts as queries, and exacts the related text and image documents from anchor-linked web pages.
Say More with Less: Understanding Prompt Learning Behaviors through Gist Compression
It finetunes the compression plugin module and uses the representations of gist tokens to emulate the raw prompts in the vanilla language model.
When coding meets ranking: A joint framework based on local learning
In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm.
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance.
Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule towards Flatter Local Minima
Learning rate is one of the most important hyper-parameters that has a significant influence on neural network training.
A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability
To better model students' exercise responses, we proposed a logarithmic linear model with three interactive strategies, which models students' exercise responses by considering the relationship among students' knowledge status, knowledge concept, and problems.
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data.
Modeling User Viewing Flow Using Large Language Models for Article Recommendation
This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles.
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective
Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training.
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments
After extensive experiments and analysis, we find that existing task orchestrating methods fail to fully utilize the heterogeneous resources, limited by inefficient CPU processing or GPU resource contention.
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates.
LR-CNN: Lightweight Row-centric Convolutional Neural Network Training for Memory Reduction
However, training its complex network is very space-consuming, since a lot of intermediate data are preserved across layers, especially when processing high-dimension inputs with a big batch size.
LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning
It proposes a dual-view legal clue reasoning mechanism, which derives from two reasoning chains of judges: 1) Law Case Reasoning, which makes legal judgments according to the judgment experiences learned from analogy/confusing legal cases; 2) Legal Ground Reasoning, which lies in matching the legal clues between criminal cases and legal decisions.
