Open-Ended Question Answering
209 papers with code • 0 benchmarks • 0 datasets
Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.
Benchmarks
These leaderboards are used to track progress in Open-Ended Question Answering
Libraries
Use these libraries to find Open-Ended Question Answering models and implementationsMost implemented papers
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
arnoweng/CheXNet
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CVPR 2017
The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.
Strategies for Pre-training Graph Neural Networks
snap-stanford/pretrain-gnns
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ICLR 2020
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
kenshohara/3D-ResNets-PyTorch
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Therefore, in the present paper, we conduct exploration study in order to improve spatiotemporal 3D CNNs as follows: (i) Recently proposed large-scale video datasets help improve spatiotemporal 3D CNNs in terms of video classification accuracy.
Hybrid Task Cascade for Instance Segmentation
open-mmlab/mmdetection
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CVPR 2019
In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.
On the Dimensionality of Word Embedding
In this paper, we provide a theoretical understanding of word embedding and its dimensionality.
Deep learning-based electroencephalography analysis: a systematic review
kylemath/DeepEEG
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To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.
Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets.
Learning to Cluster Faces on an Affinity Graph
yl-1993/learn-to-cluster
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CVPR 2019
Face recognition sees remarkable progress in recent years, and its performance has reached a very high level.
OPIEC: An Open Information Extraction Corpus
In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia.
Coresets for Data-efficient Training of Machine Learning Models
Here we develop CRAIG, a method to select a weighted subset (or coreset) of training data that closely estimates the full gradient by maximizing a submodular function.
