Binary Classification
552 papers with code • 4 benchmarks • 12 datasets
Benchmarks
These leaderboards are used to track progress in Binary Classification
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Datasets
ToLD-Br
InspiRe
TII-SSRC-23
TuPyE-Dataset
SMDG
Subtasks
Most implemented papers
Bayesian regression and Bitcoin
In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency.
Sampling Generative Networks
We introduce several techniques for sampling and visualizing the latent spaces of generative models.
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data.
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss.
wav2vec: Unsupervised Pre-training for Speech Recognition
pytorch/fairseq
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Our experiments on WSJ reduce WER of a strong character-based log-mel filterbank baseline by up to 36% when only a few hours of transcribed data is available.
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
facebookresearch/mmf
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NeurIPS 2020
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes.
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews
Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review).
DeepDTA: Deep Drug-Target Binding Affinity Prediction
hkmztrk/DeepDTA
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The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction.
Rank consistent ordinal regression for neural networks with application to age estimation
Raschka-research-group/coral-cnn
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In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy.
Notes on Noise Contrastive Estimation and Negative Sampling
Estimating the parameters of probabilistic models of language such as maxent models and probabilistic neural models is computationally difficult since it involves evaluating partition functions by summing over an entire vocabulary, which may be millions of word types in size.
