https://bitbucket.org/cbgr/inmotifin_evaluation
공식 코드inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 11
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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://bitbucket.org/fhirschberger/clustering
공식 코드Large Scale Clustering with Variational EM for Gaussian Mixture Models
https://bitbucket.org/rumschuettel/rvqe
공식 코드https://github.com/0frett/po-uncertainty-benchmarking
공식 코드Benchmarking Large Language Model Uncertainty for Prompt Optimization
https://github.com/0xade1a1de/cryptopt
공식 코드https://github.com/1041877801/COMBHelper
공식 코드COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
https://github.com/1980x/CCT
공식 코드https://github.com/1adrianb/binary-networks-pytorch
공식 코드Improved training of binary networks for human pose estimation and image recognition
https://github.com/2018cx/multi-level-ot
공식 코드https://github.com/2018cx/sinkd
공식 코드https://github.com/2hojae/mentor-kd
공식 코드Mentor-KD: Making Small Language Models Better Multi-step Reasoners
https://github.com/2younis/DFGRID
공식 코드Data-Free Generative Replay for Class-Incremental Learning on Imbalanced Data
https://github.com/2younis/dfgr
공식 코드Data-Free Generative Replay for Class-Incremental Learning on Imbalanced Data
https://github.com/3biocompbio/bcellepitope
공식 코드Critical review of conformational B-cell epitope prediction methods
https://github.com/53X/CogniNET
공식 코드Cogni-Net: Cognitive Feature Learning through Deep Visual Perception
https://github.com/594422814/CF-VGG
공식 코드Real-Time Correlation Tracking via Joint Model Compression and Transfer
https://github.com/846468230/FedACK
공식 코드https://github.com/983632847/WebUAV-3M
공식 코드https://github.com/983632847/awesome-multimodal-object-tracking
공식 코드https://github.com/AI-HPC-Research-Team/AIPerf
공식 코드https://github.com/AI-in-Cardiovascular-Medicine/nnUZoo
공식 코드https://github.com/AI4HealthUOL/bowel-sound-classification
공식 코드https://github.com/AI4WA/OpenOmniFramework
공식 코드https://github.com/AIPAG/SeqMIA
공식 코드https://github.com/AIRI-Institute/GLiRA
공식 코드GLiRA: Black-Box Membership Inference Attack via Knowledge Distillation
https://github.com/AIRS-CSR/FractalAD
공식 코드https://github.com/AISquaredInc/MultitaskNetworkExperiments
공식 코드https://github.com/AIprogrammer/AdvMix
공식 코드When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks
https://github.com/AMAAI-Lab/Music2Emotion
공식 코드Towards Unified Music Emotion Recognition across Dimensional and Categorical Models
https://github.com/AMAAI-Lab/megamusicaps
공식 코드MIRFLEX: Music Information Retrieval Feature Library for Extraction
https://github.com/ASK-Berkeley/MLFF-distill
공식 코드https://github.com/ATriantafyllopoulos/is24-interspeech09-ser-revisited
공식 코드https://github.com/Aaditya-Singh/Low-Shot-Robustness
공식 코드Benchmarking Low-Shot Robustness to Natural Distribution Shifts
https://github.com/AaltoVision/ADVIO
공식 코드https://github.com/AceCHQ/MMIQ
공식 코드MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models
https://github.com/AkonLau/ShuffleMix
공식 코드ShuffleMix: Improving Representations via Channel-Wise Shuffle of Interpolated Hidden States
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.