Papers with Code 아카이브

Diversity

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/boschresearch/issa

공식 코드

Intra-Source Style Augmentation for Improved Domain Generalization

https://github.com/boschresearch/one-shot-synthesis

공식 코드

Generating Novel Scene Compositions from Single Images and Videos

https://github.com/bowang-lab/integrao

공식 코드

Integrate Any Omics: Towards genome-wide data integration for patient stratification

https://github.com/bpucla/ibebm

공식 코드

Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification

https://github.com/braingpt-lovelab/haico

공식 코드

Confidence-weighted integration of human and machine judgments for superior decision-making

https://github.com/brainimageanalysis/impregdec

공식 코드

Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brain

https://github.com/brainsoupfactory/moment-neural-network

공식 코드

Optimal signal transmission and timescale diversity in a model of human brain operating near criticality

https://github.com/brandontrabucco/da-fusion

공식 코드

Effective Data Augmentation With Diffusion Models

https://github.com/brendanartley/genplot

공식 코드

GenPlot: Increasing the Scale and Diversity of Chart Derendering Data

https://github.com/brochier/expert_finding

공식 코드

New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert Finding

https://github.com/brucewlee/moral-value-bias

공식 코드

When Prompting Fails to Sway: Inertia in Moral and Value Judgments of Large Language Models

https://github.com/brudfors/spm_superres

공식 코드

A Tool for Super-Resolving Multimodal Clinical MRI

https://github.com/bunsenfeng/polilean

공식 코드

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

https://github.com/buptwululu/fg-rag

공식 코드

FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG

https://github.com/buseskorkmaz/fms-at-work

공식 코드

Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring

https://github.com/byeongjun-park/HarmonyView

공식 코드

HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D

https://github.com/bytedance-seed/bm-code

공식 코드

ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions

https://github.com/bytedance/confdiff

공식 코드

Protein Conformation Generation via Force-Guided SE(3) Diffusion Models

https://github.com/bytedance/e2str

공식 코드

Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer

https://github.com/bzhangGo/zero

공식 코드

Sparse Attention with Linear Units

https://github.com/c-liangyu/csval

공식 코드

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning

https://github.com/c-w-d/cascod

공식 코드

Improve Student's Reasoning Generalizability through Cascading Decomposed CoTs Distillation

https://github.com/c-w-d/meaeq

공식 코드

MeaeQ: Mount Model Extraction Attacks with Efficient Queries

https://github.com/c0natus/D3Rec

공식 코드

Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category Preferences

https://github.com/cablelabs/llmdata

공식 코드

Randomness Is All You Need: Semantic Traversal of Problem-Solution Spaces with Large Language Models

https://github.com/cairo-thws/fed_streaming

공식 코드

Sparse Uncertainty-Informed Sampling from Federated Streaming Data

https://github.com/caisa-lab/naacl2024-considerations-annotator-modeling

공식 코드

Corpus Considerations for Annotator Modeling and Scaling

https://github.com/cambridgeltl/cometa

공식 코드

COMETA: A Corpus for Medical Entity Linking in the Social Media

https://github.com/camlab-ethz/rigno

공식 코드

RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains

https://github.com/campanulabells/prem-gad

공식 코드

PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection

https://github.com/cancaries/SceneCrafter

공식 코드

Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving

https://github.com/cannylab/anthology

공식 코드

Virtual Personas for Language Models via an Anthology of Backstories

https://github.com/cannylab/vdtk

공식 코드

What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics

https://github.com/caoyu-noob/embedding_augmentation

공식 코드

Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation

https://github.com/caporaso-lab/q2-boots

공식 코드

Facilitating bootstrapped and rarefaction-based microbiome diversity analysis with q2-boots

https://github.com/caprirecsys/capri

공식 코드

CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.