Papers with Code 아카이브

Density Estimation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 14
보존된 평가 기록: 65

The goal of Density Estimation is to give an accurate description of the underlying probabilistic density distribution of an observable data set with unknown density.

Source: Contrastive Predictive Coding Based Feature for Automatic Speaker Verification

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dylran/diffusedenoisecount

공식 코드

$CrowdDiff$: Multi-hypothesis Crowd Density Estimation using Diffusion Models

https://github.com/ecker-lab/tneb_clustering

공식 코드

Hierarchical clustering with maximum density paths and mixture models

https://github.com/eerdil/task_agnostic_ood

공식 코드

Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation

https://github.com/elad-amrani/ssml

공식 코드

Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning

https://github.com/elouanargouarch/discretely-indexed-flows

공식 코드

Discretely Indexed Flows

https://github.com/emanino/plain_c_nn_benchmark

공식 코드

NeuroCodeBench: a plain C neural network benchmark for software verification

https://github.com/emorymlip/cot-flow

공식 코드

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

https://github.com/emorymlip/pcp-map

공식 코드

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

https://github.com/enyandai/ganf

공식 코드

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

https://github.com/erbloo/raan

공식 코드

Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation

https://github.com/ericstrobl/ddr

공식 코드

Dirac Delta Regression: Conditional Density Estimation with Clinical Trials

https://github.com/ericyeats/gcae-disentanglement

공식 코드

Disentangling Learning Representations with Density Estimation

https://github.com/ermongroup/BiasAndGeneralization

공식 코드

Bias and Generalization in Deep Generative Models: An Empirical Study

https://github.com/ermongroup/bgm

공식 코드

Boosted Generative Models

https://github.com/facebookresearch/ecevecce

공식 코드

Metrics of calibration for probabilistic predictions

https://github.com/facebookresearch/suncet

공식 코드

RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data

https://github.com/fagonzalezo/kdm

공식 코드

Kernel Density Matrices for Probabilistic Deep Learning

https://github.com/fagonzalezo/kdm_for_probabilistic_dl_experiments

공식 코드

Kernel Density Matrices for Probabilistic Deep Learning

https://github.com/freelunchtheorem/Conditional_Density_Estimation

공식 코드

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks

https://github.com/gabeguo/any-order-speculative-decoding

공식 코드

Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding

https://github.com/galinngeorgiev/ACE

공식 코드

Towards universal neural nets: Gibbs machines and ACE

https://github.com/gaoguangshuai/survey-for-crowd-counting

공식 코드

CNN-based Density Estimation and Crowd Counting: A Survey

https://github.com/gapsc-us/dl4art

공식 코드

Thread Counting in Plain Weave for Old Paintings Using Semi-Supervised Regression Deep Learning Models

https://github.com/gfreund123/cfil

공식 코드

A Coupled Flow Approach to Imitation Learning

https://github.com/giorgioangel/dsym

공식 코드

Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning

https://github.com/giuliotrigila/moonexample

공식 코드

A generative flow for conditional sampling via optimal transport

https://github.com/gjq100/esma

공식 코드

Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability

https://github.com/gjy3035/Awesome-Crowd-Counting

공식 코드

CNN-based Density Estimation and Crowd Counting: A Survey

https://github.com/gkw0010/gtav_head-dataset

공식 코드

Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks

https://github.com/google-deepmind/pbde

공식 코드

A density estimation perspective on learning from pairwise human preferences

https://github.com/google-research/google-research/tree/master/diffusion_distillation

공식 코드

Progressive Distillation for Fast Sampling of Diffusion Models

https://github.com/google-research/optformer

공식 코드

Decoding-based Regression

https://github.com/google-research/perceiver-ar

공식 코드

General-purpose, long-context autoregressive modeling with Perceiver AR

https://github.com/google-research/vdm

공식 코드

Variational Diffusion Models

https://github.com/gpapamak/epsilon_free_inference

공식 코드

Fast $ε$-free Inference of Simulation Models with Bayesian Conditional Density Estimation

https://github.com/gpapamak/maf

공식 코드

Masked Autoregressive Flow for Density Estimation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.