Prediction
2677 papers with code • 2 benchmarks • 3 datasets
Libraries
Use these libraries to find Prediction models and implementations
QM9
SDWPF
Bone Cement Removal with Audio-Monitoring
Most implemented papers
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Despite widespread adoption, machine learning models remain mostly black boxes.
Variational Graph Auto-Encoders
tkipf/gae
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We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE).
Deeper Depth Prediction with Fully Convolutional Residual Networks
iro-cp/FCRN-DepthPrediction
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This paper addresses the problem of estimating the depth map of a scene given a single RGB image.
Deep Interest Network for Click-Through Rate Prediction
zhougr1993/DeepInterestNetwork
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In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.
Deep Interest Evolution Network for Click-Through Rate Prediction
mouna99/dien
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Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt
Vision Transformers for Dense Prediction
huggingface/transformers
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ICCV 2021
We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks.
Curiosity-driven Exploration by Self-supervised Prediction
pathak22/noreward-rl
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ICML 2017
In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether.
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
A Deep Reinforced Model for Abstractive Summarization
theamrzaki/text_summurization_abstractive_methods
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ICLR 2018
We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL).
Link Prediction Based on Graph Neural Networks
muhanzhang/SEAL
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NeurIPS 2018
The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs.
