Structured Prediction
190 papers with code • 1 benchmarks • 6 datasets
Structured Prediction is an area of machine learning focusing on representations of spaces with combinatorial structure, and algorithms for inference and parameter estimation over these structures. Core methods include both tractable exact approaches like dynamic programming and spanning tree algorithms as well as heuristic techniques such as linear programming relaxations and greedy search.
Libraries
Use these libraries to find Structured Prediction models and implementations
MNIST
ListOps
CFQ
SciREX
WIKIOG
WBM
Most implemented papers
Convolutional Pose Machines
Pose Machines provide a sequential prediction framework for learning rich implicit spatial models.
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
fartashf/vsepp
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We present a new technique for learning visual-semantic embeddings for cross-modal retrieval.
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution.
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
vishalrk1/SkimLit
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EACL 2017
Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually.
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
iesl/dilated-cnn-ner
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EMNLP 2017
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs.
Thinking Fast and Slow with Deep Learning and Tree Search
richemslie/galvanise_zero
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NeurIPS 2017
Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans.
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
crazydonkey200/neural-symbolic-machines
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NeurIPS 2018
We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i. i. d.
Deep Metric Learning via Lifted Structured Feature Embedding
Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning.
Input Convex Neural Networks
We show that many existing neural network architectures can be made input-convex with a minor modification, and develop specialized optimization algorithms tailored to this setting.
