https://github.com/migrationsKB/MGKB
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 11
- 보존된 평가 기록
- 90
Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mindspore-ai/models/blob/master/official/cv/retinanet/
https://github.com/mindspore-ai/models/blob/master/research/cv/retinanet_resnet152
https://github.com/mindspore-ai/models/tree/master/official/nlp/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/mindspore-ai/models/tree/master/research/cv/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/mindspore-ai/models/tree/master/research/nlp/ternarybert
https://github.com/mindspore-courses/ABSA-MindSpore
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
https://github.com/minerllabs/minerl
https://github.com/ming024/FastSpeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/minglunhan/cif-hieradist
https://github.com/mingyukim87/synergynerf
DistiLLM: Towards Streamlined Distillation for Large Language Models
https://github.com/minhanh151/pre
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/minhanh151/respro
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/minhdua/PHONES
https://github.com/mit-spark/kimera-multi
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
https://github.com/mitmedialab/viznet
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
https://github.com/mkavim/finetune_bert
https://github.com/mkocabas/focal-loss-keras
https://github.com/ml-energy/zeus
The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
https://github.com/ml-jku/cloob
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlbio-epfl/turtle
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlcommons/chakra
Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
https://github.com/mlcommons/inference
https://github.com/mlcommons/submissions_algorithms
https://github.com/mlcommons/training
https://github.com/mlf-core/liver-ct-segmentation
https://github.com/mlfoundations/Evalchemy
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
https://github.com/mlfoundations/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlr-org/mlr
Benchmarking time series classification -- Functional data vs machine learning approaches
https://github.com/mmaaz60/EdgeNeXt
Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results
https://github.com/mmore500/signalgp-lite
SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications
https://github.com/mmurrad/eeg-to-text
https://github.com/moallafatma/Breast_Cancer_Detection_Classification
https://github.com/mobarakol/AP-MTL
https://github.com/mobarakol/Surgical_SceneGraph_Generation
Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding
https://github.com/modeltc/outlier_suppression_plus
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.