https://github.com/hazyresearch/based
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Bags of Local Convolutional Features for Scalable Instance Search
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
Contrastive Learning of Medical Visual Representations from Paired Images and Text
ExprGAN: Facial Expression Editing with Controllable Expression Intensity
Phase Retrieval Using Conditional Generative Adversarial Networks
Retrieve and Refine: Improved Sequence Generation Models For Dialogue
Medical Image Retrieval using Deep Convolutional Neural Network
Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
Extending Context Window of Large Language Models via Positional Interpolation
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing
code2seq: Generating Sequences from Structured Representations of Code
Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Real-time 3D Nanoscale Coherent Imaging via Physics-aware Deep Learning
Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.