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Found 15 papers, 5 papers with code
Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN
The power and the potential of deep learning models attract many researchers to design advanced and sophisticated architectures.
Exploring space efficiency in a tree-based linear model for extreme multi-label classification
Many past works assume that storing the model is not affordable and apply techniques such as pruning to save space, which may lead to performance loss.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
+3
A Step-by-step Introduction to the Implementation of Automatic Differentiation
Automatic differentiation is a key component in deep learning.
Linear Classifier: An Often-Forgotten Baseline for Text Classification
Large-scale pre-trained language models such as BERT are popular solutions for text classification.
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification
Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures.
Ranked #1 on
Medical Code Prediction
on MIMIC-IV-ICD10-top50
Trainable Weight Averaging: A General Approach for Subspace Training
Training deep neural networks (DNNs) in low-dimensional subspaces is a promising direction for achieving efficient training and better generalization performance.
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations
In practice such ground truth information is rarely available, but we point out that such an inappropriate setting is now ubiquitous in this research area.
Parameter Selection: Why We Should Pay More Attention to It
In the topic of multi-label classification for medical code prediction, one influential paper conducted a proper parameter selection on a set, but when moving to a subset of frequently occurring labels, the authors used the same parameters without a separate tuning.
Efficient Optimization Methods for Extreme Similarity Learning with Nonlinear Embeddings
We study the problem of learning similarity by using nonlinear embedding models (e. g., neural networks) from all possible pairs.
Newton Methods for Convolutional Neural Networks
Deep learning involves a difficult non-convex optimization problem, which is often solved by stochastic gradient (SG) methods.
Distributed Newton Methods for Deep Neural Networks
First, to reduce the communication cost, we propose a diagonalization method such that an approximate Newton direction can be obtained without communication between machines.
Field-aware factorization machines for CTR prediction
Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task.
Large-Scale Video Summarization Using Web-Image Priors
Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently.
Dense Non-rigid Point-Matching Using Random Projections
We present a robust and efficient technique for matching dense sets of points undergoing non-rigid spatial transformations.
A Bayesian Approximation Method for Online Ranking
Experiments on game data show that the accuracy of our approach is competitive with state of the art systems such as TrueSkill, but the running time as well as the code is much shorter.
