https://github.com/sec-bench/sec-bench
공식 코드SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks
Papers with Code 아카이브
Knowledge Distillation
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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://github.com/secanth/massart
공식 코드https://github.com/seedatnabeel/h-cat
공식 코드https://github.com/seekingdream/ppm
공식 코드PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models
https://github.com/seekingdream/static-to-dynamic-llmeval
공식 코드https://github.com/seephys/seephys-project
공식 코드SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning
https://github.com/segmind/ssd-1b
공식 코드Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss
https://github.com/selenashe/scone
공식 코드ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
https://github.com/sense-x/sit
공식 코드https://github.com/sensein/structsense
공식 코드https://github.com/senwang98/monoskd
공식 코드https://github.com/seohongpark/ogbench
공식 코드https://github.com/seongku-kang/hetcomp_www23
공식 코드Distillation from Heterogeneous Models for Top-K Recommendation
https://github.com/seonokkim/3dyoga90
공식 코드3DYoga90: A Hierarchical Video Dataset for Yoga Pose Understanding
https://github.com/seonwhee-genome/rvernet
공식 코드https://github.com/serval-uni-lu/tabularbench
공식 코드TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
https://github.com/serverless-federated-learning/fedless
공식 코드Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
https://github.com/servicenow/agentlab
공식 코드https://github.com/servicenow/broad-openood
공식 코드Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection
https://github.com/servicenow/browsergym
공식 코드https://github.com/servicenow/fm2ds
공식 코드https://github.com/seu-vipgroup/fg-bmk
공식 코드Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation
https://github.com/seung-b/fl-psg
공식 코드Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation
https://github.com/seznam/czech-semantic-embedding-models
공식 코드Some Like It Small: Czech Semantic Embedding Models for Industry Applications
https://github.com/sfarhat/dapt
공식 코드On the Surprising Efficacy of Distillation as an Alternative to Pre-Training Small Models
https://github.com/sfofgalaxy/jalmbench
공식 코드JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models
https://github.com/shadowkiller33/contrast-instruction
공식 코드https://github.com/shailja-thakur/vgen
공식 코드Benchmarking Large Language Models for Automated Verilog RTL Code Generation
https://github.com/shakib-io/kd-lesions
공식 코드https://github.com/shams-mehdi/aib9_openmm
공식 코드A survey of probabilistic generative frameworks for molecular simulations
https://github.com/shanay-mehta/anomaly-benchmarking
공식 코드Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
https://github.com/shanghai-digital-brain-laboratory/db-football
공식 코드An Empirical Study on Google Research Football Multi-agent Scenarios
https://github.com/shaoeric/Decoupled-Knowledge-with-Ensemble-Learning-for-Online-Distillation
공식 코드Decoupled Knowledge with Ensemble Learning for Online Distillation
https://github.com/shaoeric/multi-granularity-distillation
공식 코드https://github.com/shaofeifei11/kd-ci-cam
공식 코드Knowledge-guided Causal Intervention for Weakly-supervised Object Localization
https://github.com/shaojiawei07/selective-fd
공식 코드Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.