https://github.com/ebalti/Double-Kappa-Mu-Shadowed-Distribution
공식 코드PLS for V2I Communications Using Friendly Jammer and Double kappa-mu Shadowed Fading
Papers with Code 아카이브
Diversity
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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/echostatements/decompose
공식 코드https://github.com/edisonni-hku/mtl4finance
공식 코드When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP
https://github.com/effl-lab/taco
공식 코드Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
https://github.com/eglxiang/vgg_face
공식 코드https://github.com/eglxiang/ytf
공식 코드https://github.com/egojr/optagan
공식 코드https://github.com/elenairene/cbem
공식 코드Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error Prediction
https://github.com/eleninisioti/GrowNeuralNets
공식 코드Growing Artificial Neural Networks for Control: the Role of Neuronal Diversity
https://github.com/eleninisioti/nicheconstructionmodel
공식 코드Dynamics of niche construction in adaptable populations evolving in diverse environments
https://github.com/eleutherai/gpt-neox
공식 코드KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation
https://github.com/ellarabi/linguistic_decline
공식 코드https://github.com/elmerzhouhaohao/AMPSO
공식 코드https://github.com/eltanin4/trophic_gut
공식 코드Evidence for a multi-level trophic organization of the human gut microbiome
https://github.com/em22ad/CSI-Fingerprint-Indoor-Localization
공식 코드Hybrid CNN-LSTM based Indoor Pedestrian Localization with CSI Fingerprint Maps
https://github.com/emily-halina/brisket
공식 코드Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI
https://github.com/emilydolson/phylodiversity-metrics-in-EC-GPTP-2021
공식 코드What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?
https://github.com/emorynlp/aave-chat
공식 코드Finding A Voice: Evaluating African American Dialect Generation for Chatbot Technology
https://github.com/emrys365/se-scaling
공식 코드Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech Enhancement
https://github.com/endo-yuki-t/DiversifyingSMIS
공식 코드Diversifying Semantic Image Synthesis and Editing via Class- and Layer-wise VAEs
https://github.com/endywon/texture-reformer
공식 코드Texture Reformer: Towards Fast and Universal Interactive Texture Transfer
https://github.com/ensta-u2is/torch-uncertainty
공식 코드https://github.com/eoghancunn/facilitating_idr
공식 코드Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender Systems
https://github.com/epignatelli/navix
공식 코드https://github.com/ergastialex/ldm-diffusion-sem
공식 코드Controllable Face Synthesis with Semantic Latent Diffusion Models
https://github.com/ergastialex/sca-dm
공식 코드Controllable Face Synthesis with Semantic Latent Diffusion Models
https://github.com/ergowho/firefly2.0
공식 코드https://github.com/eric11eca/disco
공식 코드DISCO: Distilling Counterfactuals with Large Language Models
https://github.com/ericacoppolillo/explore
공식 코드https://github.com/ericberlow/creative-diversity
공식 코드Creative Diversity: Patterns in the Creative Habits of ~10,000 People
https://github.com/erizmr/Complex-locomotion-skill-learning-via-differentiable-physics
공식 코드Complex Locomotion Skill Learning via Differentiable Physics
https://github.com/ermongroup/ODS
공식 코드Diversity can be Transferred: Output Diversification for White- and Black-box Attacks
https://github.com/ernie-research/cd-rlhf
공식 코드https://github.com/esizikova/medsynth_public
공식 코드Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction
https://github.com/estherrolf/representation-matters
공식 코드Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
https://github.com/ethanlq/scma-codebook
공식 코드Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed Users
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.