Papers in this area
Showing 1–10 of 10 papers
| Task | Papers | Results |
|---|---|---|
| Neural Architecture Search Neural architecture search (NAS) is a technique for automati… | 1,915 | 424 |
| Long-tail Learning Long-tailed learning, one of the most challenging problems i… | 131 | 421 |
| Unsupervised Domain Adaptation Unsupervised Domain Adaptation is a learning framework to tr… | 1,951 | 393 |
| Domain Adaptation Domain Adaptation is the task of adapting models across doma… | 6,439 | 391 |
| Multi-Label Classification multilabel graph classification with highest result | 1,198 | 302 |
| Out-of-Distribution Detection Detect out-of-distribution or anomalous examples. | 888 | 269 |
| Link Property Prediction | 13 | 160 |
| Classification Classification is the task of categorizing a set of data int… | 12,815 | 134 |
| Continual Learning Continual Learning (also known as Incremental Learning, Life… | 2,644 | 133 |
| Trajectory Prediction Trajectory Prediction is the problem of predicting the short… | 1,004 | 132 |
| Metric Learning The goal of Metric Learning is to learn a representation fun… | 1,648 | 122 |
| Incremental Learning Incremental learning aims to develop artificially intelligen… | 1,371 | 112 |
| Knowledge Distillation Knowledge distillation is the process of transferring knowle… | 4,240 | 90 |
| Zero-Shot Learning Zero-shot learning (ZSL) is a model's ability to detect clas… | 1,864 | 84 |
| Cross-Domain Few-Shot | 141 | 76 |
| Density Estimation The goal of Density Estimation is to give an accurate descri… | 1,394 | 65 |
| Federated Learning Federated Learning is a machine learning approach that allow… | 6,771 | 61 |
| Few-Shot Learning Few-Shot Learning is an example of meta-learning, where a le… | 2,964 | 52 |
| Universal Domain Adaptation | 55 | 51 |
| Quantization Quantization is a promising technique to reduce the computat… | 4,925 | 37 |
| Stochastic Optimization Stochastic Optimization is the task of optimizing certain ob… | 1,387 | 35 |
| Network Pruning Network Pruning is a popular approach to reduce a heavy netw… | 534 | 35 |
| Adversarial Robustness Adversarial Robustness evaluates the vulnerabilities of mach… | 1,746 | 33 |
| Multiple Instance Learning Multiple Instance Learning is a type of weakly supervised le… | 744 | 28 |
| Adversarial Defense Competitions with currently unpublished results: - [TrojAI](… | 403 | 28 |
| Social Media Popularity Prediction Social Media Popularity Prediction (SMPP) aims to predict th… | 7 | 27 |
| Clustering Algorithms Evaluation | 12 | 25 |
| Point Processes | 541 | 22 |
| Semantic Similarity The main objective Semantic Similarity is to measure the dis… | 1,564 | 21 |
| General Classification Algorithms trying to solve the general task of classificatio… | 14,581 | 19 |
| Source-Free Domain Adaptation Source-Free Domain Adaptation (SFDA) is a domain adaptation … | 188 | 19 |
| Self-Supervised Learning Self-Supervised Learning is proposed for utilizing unlabeled… | 5,044 | 18 |
| Crop Classification | 48 | 18 |
| Atomic number classification Predict the atomic number of a node in a molecular/material/… | 1 | 18 |
| Contrastive Learning Contrastive Learning is a deep learning technique for unsupe… | 6,661 | 17 |
| Multi-Task Learning Multi-task learning aims to learn multiple different tasks s… | 3,687 | 16 |
| Anomaly Classification Anomaly Classification is the task of identifying and catego… | 72 | 16 |
| Distance regression Prediction of the distance between connected nodes in molecu… | 19 | 16 |
| regression | 9,424 | 15 |
| Class Incremental Learning | 634 | 15 |
| GPS Embeddings GPS Embeddings is the collective name for a set of feature-l… | 1 | 15 |
| Binary Classification | 2,574 | 14 |
| Model Compression Model Compression is an actively pursued area of research ov… | 1,356 | 14 |
| Multi-target Domain Adaptation The idea of Multi-target Domain Adaptation is to adapt a mod… | 39 | 14 |
| Interpretability Techniques for Deep Learning | 25 | 14 |
| MMR total Sum of all scores of the 11 distinct tasks involving texts, … | 12 | 14 |
| Core set discovery A core set in machine learning is defined as the minimal set… | 1 | 14 |
| Meta-Learning Meta-learning is a methodology considered with "learning to … | 3,569 | 13 |
| Saliency Prediction A saliency map is a model that predicts eye fixations on a v… | 268 | 13 |
| Feature Importance | 890 | 12 |