| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Paper Title | Primary Subject Area | Secondary Subject Areas | Clustering Subject Preference | ||||||||||||||||||||||
2 | Continuous Surface Embeddings | Applications -> Computer Vision | Applications -> Body Pose, Face, and Gesture Analysis; Deep Learning -> Embedding Approaches | Vision | ||||||||||||||||||||||
3 | Improving model calibration with accuracy versus uncertainty optimization | Algorithms -> Uncertainty Estimation | Algorithms -> Classification; Deep Learning -> Efficient Training Methods; Optimization -> Stochastic Optimization; Probabilistic Methods -> Variational Inference | Deep learning | ||||||||||||||||||||||
4 | Few-shot Image Generation with Elastic Weight Consolidation | Applications -> Computer Vision | Deep Learning -> Generative Models | Deep learning | ||||||||||||||||||||||
5 | Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks | Deep Learning -> Analysis and Understanding of Deep Networks | Theory -> Statistical Learning Theory | Theory (including computational and statistical analyses) | ||||||||||||||||||||||
6 | Quantitative Propagation of Chaos for SGD in Wide Neural Networks | Theory -> Large Deviations and Asymptotic Analysis | Theory (including computational and statistical analyses) | |||||||||||||||||||||||
7 | Stochastic Optimization for Performative Prediction | Optimization -> Stochastic Optimization | Optimization -> Convex Optimization; Theory -> Statistical Learning Theory | Core machine learning methods (e.g., supervised learning, ranking, clustering, metric learning, etc.) | ||||||||||||||||||||||
8 | Explicit Regularisation in Gaussian Noise Injections | Deep Learning -> Analysis and Understanding of Deep Networks | Deep learning | |||||||||||||||||||||||
9 | Dual Instrumental Variable Regression | Probabilistic Methods -> Causal Inference | Algorithms -> Kernel Methods; Optimization -> Stochastic Optimization | Causality | ||||||||||||||||||||||
10 | Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness | Algorithms -> Multitask and Transfer Learning | Algorithms -> Adversarial Learning; Applications -> Computer Vision; Deep Learning -> Efficient Training Methods; Theory -> Models of Learning and Generalization ; Theory -> Regularization | Vision | ||||||||||||||||||||||
11 | Strongly Incremental Constituency Parsing with Graph Neural Networks | Applications | Applications -> Natural Language Processing | Natural language processing | ||||||||||||||||||||||
12 | AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients | Optimization -> Stochastic Optimization | Deep Learning -> Efficient Training Methods; Optimization -> Non-Convex Optimization | Optimization Methods (continuous or discrete) | ||||||||||||||||||||||
13 | CoinPress: Practical Private Mean and Covariance Estimation | Social Aspects of Machine Learning -> Privacy, Anonymity, and Security | Social aspects of machine learning (e.g., fairness, safety, privacy) | |||||||||||||||||||||||
14 | The Discrete Gaussian for Differential Privacy | Social Aspects of Machine Learning -> Privacy, Anonymity, and Security | Social aspects of machine learning (e.g., fairness, safety, privacy) | |||||||||||||||||||||||
15 | Private Identity Testing for High-Dimensional Distributions | Social Aspects of Machine Learning -> Privacy, Anonymity, and Security | Theory -> Statistical Learning Theory | Social aspects of machine learning (e.g., fairness, safety, privacy) | ||||||||||||||||||||||
16 | Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding | Algorithms -> Representation Learning | Algorithms -> Large Scale Learning; Algorithms -> Metric Learning | Core machine learning methods (e.g., supervised learning, ranking, clustering, metric learning, etc.) | ||||||||||||||||||||||
17 | Learning Kernel Tests Without Data Splitting | Algorithms -> Kernel Methods | Algorithms -> Model Selection and Structure Learning | Core machine learning methods (e.g., supervised learning, ranking, clustering, metric learning, etc.) | ||||||||||||||||||||||
18 | Generative View Synthesis: From Single-view Semantics to Novel-view Images | Applications -> Computer Vision | Applications -> Computational Photography; Deep Learning -> Generative Models | Vision | ||||||||||||||||||||||
19 | Rational neural networks | Deep Learning -> Analysis and Understanding of Deep Networks | Theory -> Hardness of Learning and Approximations | Deep learning | ||||||||||||||||||||||
20 | Towards More Practical Adversarial Attacks on Graph Neural Networks | Algorithms | Algorithms -> Adversarial Learning; Optimization -> Submodular Optimization; Social Aspects of Machine Learning -> AI Safety | Deep learning | ||||||||||||||||||||||
21 | Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning | Deep Learning -> Visualization, Interpretability, and Explainability | Applications -> Computer Vision | Deep learning | ||||||||||||||||||||||
22 | Space-Time Correspondence as a Contrastive Random Walk | Applications -> Computer Vision | Applications -> Tracking and Motion in Video; Applications -> Video Analysis | |||||||||||||||||||||||
23 | Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement | Reinforcement Learning and Planning | Reinforcement Learning and Planning -> Reinforcement Learning | Reinforcement learning and planning | ||||||||||||||||||||||
24 | Self-supervised Co-Training for Video Representation Learning | Algorithms -> Representation Learning | Algorithms -> Unsupervised Learning; Applications -> Activity and Event Recognition; Applications -> Video Analysis | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
25 | Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D | Data, Challenges, Implementations, and Software -> Data Sets or Data Repositories | Applications -> Computer Vision | Vision | ||||||||||||||||||||||
26 | Is normalization indispensable for training deep neural network? | Deep Learning | Deep Learning -> Analysis and Understanding of Deep Networks | Deep learning | ||||||||||||||||||||||
27 | Efficient Exact Verification of Binarized Neural Networks | Algorithms -> Adversarial Learning | Verification of neural networks | |||||||||||||||||||||||
28 | ConvBERT: Improving BERT with Span-based Dynamic Convolution | Applications -> Natural Language Processing | Deep Learning -> Attention Models | Natural language processing | ||||||||||||||||||||||
29 | On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law | Applications -> Visual Question Answering | Data, Challenges, Implementations, and Software -> Benchmarks | Vision | ||||||||||||||||||||||
30 | Labelling unlabelled videos from scratch with multi-modal self-supervision | Applications -> Computer Vision | Algorithms -> Clustering; Algorithms -> Multimodal Learning | Vision | ||||||||||||||||||||||
31 | Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot | Deep Learning | Deep Learning -> Analysis and Understanding of Deep Networks; Deep Learning -> Efficient Inference Methods; Deep Learning -> Efficient Training Methods | Deep learning | ||||||||||||||||||||||
32 | On the equivalence of molecular graph convolution and molecular wave function with poor basis set | Deep Learning -> Supervised Deep Networks | Other applications (e.g., robotics, biology, climate, finance) | |||||||||||||||||||||||
33 | Generative 3D Part Assembly via Dynamic Graph Learning | Applications -> Computer Vision | Deep Learning -> Generative Models | Vision | ||||||||||||||||||||||
34 | Prophet Attention: Predicting Attention with Future Attention | Applications -> Computer Vision | Applications -> Natural Language Processing | Natural language processing | ||||||||||||||||||||||
35 | Heuristic Domain Adaptation | Algorithms -> Multitask and Transfer Learning | Algorithms -> Adversarial Learning; Algorithms -> Classification; Algorithms -> Semi-Supervised Learning; Applications -> Computer Vision | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
36 | A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings | Algorithms -> Kernel Methods | Theory -> Statistical Learning Theory | Theory (including computational and statistical analyses) | ||||||||||||||||||||||
37 | PLANS: Neuro-Symbolic Program Learning from Videos | Algorithms -> Program Induction | Applications -> Activity and Event Recognition; Applications -> Video Analysis | Reasoning | ||||||||||||||||||||||
38 | AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection | Applications -> Body Pose, Face, and Gesture Analysis | Applications -> Computer Vision | Vision | ||||||||||||||||||||||
39 | Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows | Applications -> Computational Biology and Bioinformatics | Algorithms -> Representation Learning; Deep Learning -> Efficient Inference Methods; Probabilistic Methods -> Graphical Models; Probabilistic Methods -> Variational Inference | Other applications (e.g., robotics, biology, climate, finance) | ||||||||||||||||||||||
40 | The Adaptive Complexity of Maximizing a Gross Substitutes Valuation | Theory -> Game Theory and Computational Economics | Theory (including computational and statistical analyses) | |||||||||||||||||||||||
41 | Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher | Deep Learning | Deep Learning -> Analysis and Understanding of Deep Networks | Theory (including computational and statistical analyses) | ||||||||||||||||||||||
42 | Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder | Deep Learning -> Deep Autoencoders | Algorithms -> Representation Learning; Algorithms -> Uncertainty Estimation; Algorithms -> Unsupervised Learning | Deep learning | ||||||||||||||||||||||
43 | Bayesian Probabilistic Numerical Integration with Tree-Based Models | Probabilistic Methods -> Bayesian Nonparametrics | Algorithms -> Active Learning | Probabilistic methods and inference | ||||||||||||||||||||||
44 | Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization | Applications -> Object Detection | Applications -> Computer Vision | Vision | ||||||||||||||||||||||
45 | Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning | Reinforcement Learning and Planning -> Exploration | Theory | Reinforcement learning and planning | ||||||||||||||||||||||
46 | Probabilistic Time Series Forecasting with Shape and Temporal Diversity | Applications -> Time Series Analysis | Deep Learning -> Predictive Models | Deep learning | ||||||||||||||||||||||
47 | Decision-Making with Auto-Encoding Variational Bayes | Probabilistic Methods -> Variational Inference | Deep Learning -> Generative Models | Probabilistic methods and inference | ||||||||||||||||||||||
48 | RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist | Applications -> Computational Biology and Bioinformatics | Deep Learning -> Attention Models; Probabilistic Methods -> Graphical Models | Other applications (e.g., robotics, biology, climate, finance) | ||||||||||||||||||||||
49 | DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles | Algorithms -> Boosting and Ensemble Methods | Deep Learning; Social Aspects of Machine Learning -> AI Safety | Social aspects of machine learning (e.g., fairness, safety, privacy) | ||||||||||||||||||||||
50 | CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations | Applications -> Computer Vision | Applications -> Visual Scene Analysis and Interpretation | Vision | ||||||||||||||||||||||
51 | Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation | Applications -> Image Segmentation | Algorithms -> Few-Shot Learning | Vision | ||||||||||||||||||||||
52 | Task-Oriented Feature Distillation | Deep Learning -> Efficient Inference Methods | Algorithms -> Classification; Algorithms -> Representation Learning | Deep learning | ||||||||||||||||||||||
53 | Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback | Applications -> Recommender Systems | Algorithms -> Collaborative Filtering; Theory -> Information Theory | Core machine learning methods (e.g., supervised learning, ranking, clustering, metric learning, etc.) | ||||||||||||||||||||||
54 | Sample Complexity of Uniform Convergence for Multicalibration | Social Aspects of Machine Learning | Social Aspects of Machine Learning -> Fairness, Accountability, and Transparency | Social aspects of machine learning (e.g., fairness, safety, privacy) | ||||||||||||||||||||||
55 | What if Neural Networks had SVDs? | Algorithms | Deep Learning | Deep learning | ||||||||||||||||||||||
56 | Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID | Algorithms -> Unsupervised Learning | Algorithms -> Representation Learning; Algorithms -> Semi-Supervised Learning; Applications -> Computer Vision; Applications -> Object Recognition | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
57 | Learning from Positive and Unlabeled Data with Arbitrary Positive Shift | Algorithms -> Semi-Supervised Learning | Algorithms -> Classification | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
58 | Kalman Filtering Attention for User Behavior Modeling in CTR Prediction | Deep Learning -> Attention Models | Algorithms -> Uncertainty Estimation; Applications -> Recommender Systems; Probabilistic Methods -> Bayesian Theory; Probabilistic Methods -> Graphical Models | Deep learning | ||||||||||||||||||||||
59 | Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts | Algorithms -> Large Scale Learning | Algorithms -> Communication- or Memory-Bounded Learning; Applications -> Hardware and Systems; Deep Learning | Deep learning | ||||||||||||||||||||||
60 | Adversarially Robust Few-Shot Learning: A Meta-Learning Approach | Algorithms -> Meta-Learning | Algorithms -> Adversarial Learning | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
61 | Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency | Applications -> Computational Photography | Applications -> Computer Vision; Deep Learning | Vision | ||||||||||||||||||||||
62 | One-bit Supervision for Image Classification | Algorithms -> Classification | Algorithms -> Active Learning; Algorithms -> Semi-Supervised Learning; Applications -> Computer Vision | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
63 | Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning | Deep Learning -> Optimization for Deep Networks | Deep Learning -> Analysis and Understanding of Deep Networks; Optimization -> Non-Convex Optimization | Deep learning | ||||||||||||||||||||||
64 | RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference | Deep Learning -> Efficient Inference Methods | Applications -> Computer Vision; Deep Learning -> Recurrent Networks | Resource aware machine learning | ||||||||||||||||||||||
65 | Self-Supervised Relationship Probing | Algorithms -> Representation Learning | Algorithms -> Multimodal Learning; Applications -> Visual Question Answering; Deep Learning -> Visualization, Interpretability, and Explainability | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
66 | SCOP: Scientific Control for Reliable Neural Network Pruning | Deep Learning | Algorithms -> Classification; Deep Learning -> CNN Architectures; Deep Learning -> Efficient Inference Methods; Deep Learning -> Optimization for Deep Networks | Deep learning | ||||||||||||||||||||||
67 | Group Contextual Encoding for 3D Point Clouds | Applications -> Computer Vision | Applications -> Object Detection; Applications -> Robotics | Vision | ||||||||||||||||||||||
68 | Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation | Algorithms -> Multitask and Transfer Learning | Applications -> Computer Vision; Applications -> Image Segmentation; Deep Learning -> Adversarial Networks | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
69 | Pruning Filter in Filter | Deep Learning | Deep Learning -> CNN Architectures; Deep Learning -> Efficient Inference Methods; Deep Learning -> Efficient Training Methods | Deep learning | ||||||||||||||||||||||
70 | Learning to Orient Surfaces by Self-supervised Spherical CNNs | Applications -> Computer Vision | Algorithms -> Unsupervised Learning | Vision | ||||||||||||||||||||||
71 | Beta R-CNN: Looking into Pedestrian Detection from Another Perspective | Applications -> Computer Vision | Algorithms -> Representation Learning | Vision | ||||||||||||||||||||||
72 | Continual Learning with Node-Importance based Adaptive Group Sparse Regularization | Algorithms -> Continual Learning | Algorithms -> Multitask and Transfer Learning; Deep Learning; Optimization -> Convex Optimization; Reinforcement Learning and Planning -> Reinforcement Learning | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
73 | HOI Analysis: Integrating and Decomposing Human-Object Interaction | Applications -> Activity and Event Recognition | Applications -> Computer Vision; Neuroscience and Cognitive Science -> Perception | Vision | ||||||||||||||||||||||
74 | Generalised Bayesian Filtering via Sequential Monte Carlo | Probabilistic Methods | Applications -> Signal Processing; Probabilistic Methods -> Latent Variable Models; Probabilistic Methods -> MCMC | Probabilistic methods and inference | ||||||||||||||||||||||
75 | A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection | Applications -> Object Detection | Vision | |||||||||||||||||||||||
76 | StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks | Applications -> Computational Social Science | Algorithms -> Kernel Methods; Algorithms -> Structured Prediction; Optimization -> Submodular Optimization; Theory -> Hardness of Learning and Approximations | Other applications (e.g., robotics, biology, climate, finance) | ||||||||||||||||||||||
77 | PAC-Bayes Analysis Beyond the Usual Bounds | Theory -> Statistical Learning Theory | Algorithms -> Classification; Algorithms -> Regression; Algorithms -> Stochastic Methods | |||||||||||||||||||||||
78 | Fast and Flexible Temporal Point Processes with Triangular Maps | Probabilistic Methods | Algorithms -> Density Estimation; Applications -> Time Series Analysis; Deep Learning -> Generative Models; Deep Learning -> Predictive Models; Probabilistic Methods -> Variational Inference | Probabilistic methods and inference | ||||||||||||||||||||||
79 | Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis | Reinforcement Learning and Planning -> Decision and Control | Applications -> Body Pose, Face, and Gesture Analysis; Applications -> Computer Vision; Applications -> Motor Control | |||||||||||||||||||||||
80 | Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes | Reinforcement Learning and Planning -> Reinforcement Learning | Optimization -> Stochastic Optimization | Reinforcement learning and planning | ||||||||||||||||||||||
81 | Sparse Learning with CART | Algorithms -> Regression | Theory -> Statistical Learning Theory | Core machine learning methods (e.g., supervised learning, ranking, clustering, metric learning, etc.) | ||||||||||||||||||||||
82 | Learning About Objects by Learning to Interact with Them | Applications -> Visual Scene Analysis and Interpretation | Applications -> Computer Vision | Vision | ||||||||||||||||||||||
83 | Softmax Deep Double Deterministic Policy Gradients | Reinforcement Learning and Planning -> Reinforcement Learning | Reinforcement learning and planning | |||||||||||||||||||||||
84 | LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond | Applications -> Computer Vision | Algorithms -> Regression; Applications -> Denoising; Deep Learning -> Efficient Inference Methods; Deep Learning -> Supervised Deep Networks | Vision | ||||||||||||||||||||||
85 | Unfolding the Alternating Optimization for Blind Super Resolution | Applications -> Computer Vision | Applications -> Denoising; Applications -> Information Retrieval | Vision | ||||||||||||||||||||||
86 | Assessing SATNet's Ability to Solve the Symbol Grounding Problem | Neuroscience and Cognitive Science -> Cognitive Science | Algorithms -> Relational Learning; Algorithms -> Representation Learning; Neuroscience and Cognitive Science -> Reasoning | Deep learning | ||||||||||||||||||||||
87 | Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation | Deep Learning -> Generative Models | Applications -> Computer Vision | Deep learning | ||||||||||||||||||||||
88 | Practical Quasi-Newton Methods for Training Deep Neural Networks | Deep Learning | Deep Learning -> Optimization for Deep Networks | Deep learning | ||||||||||||||||||||||
89 | RANet: Region Attention Network for Semantic Segmentation | Applications -> Computer Vision | Applications -> Image Segmentation | Vision | ||||||||||||||||||||||
90 | HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory | Applications -> Information Retrieval | Algorithms -> Similarity and Distance Learning; Applications -> Hardware and Systems | Datasets, challenges, software | ||||||||||||||||||||||
91 | Online Adaptation for Consistent Mesh Reconstruction in the Wild | Applications -> Computer Vision | Applications -> Video Analysis | Vision | ||||||||||||||||||||||
92 | Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs | Algorithms -> Representation Learning | Algorithms -> Relational Learning; Algorithms -> Semi-Supervised Learning; Applications -> Network Analysis; Deep Learning -> CNN Architectures | Deep learning | ||||||||||||||||||||||
93 | PIE-NET: Parametric Inference of Point Cloud Edges | Applications | Applications -> Computer Vision; Applications -> Object Detection | Vision | ||||||||||||||||||||||
94 | Kernel Based Progressive Distillation for Adder Neural Networks | Deep Learning | Algorithms -> Classification; Applications -> Computer Vision; Deep Learning -> CNN Architectures; Deep Learning -> Efficient Inference Methods | Deep learning | ||||||||||||||||||||||
95 | Self-Supervised Learning by Cross-Modal Audio-Video Clustering | Applications -> Video Analysis | Algorithms -> Unsupervised Learning; Applications -> Activity and Event Recognition; Applications -> Computer Vision | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
96 | Statistical and Topological Properties of Sliced Probability Divergences | Theory -> Frequentist Statistics | Theory (including computational and statistical analyses) | |||||||||||||||||||||||
97 | UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection | Applications -> Object Detection | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | |||||||||||||||||||||||
98 | Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model | Reinforcement Learning and Planning | Algorithms -> Sparsity and Compressed Sensing; Reinforcement Learning and Planning -> Model-Based RL; Reinforcement Learning and Planning -> Planning; Theory -> High-Dimensional Inference; Theory -> Statistical Learning Theory | Reinforcement learning and planning | ||||||||||||||||||||||
99 | Task-Robust Model-Agnostic Meta-Learning | Algorithms -> Meta-Learning | Optimization -> Stochastic Optimization | Learning with limited supervision (e.g., unsupervised learning, active learning, few-shot, meta-learning, transfer learning, etc.) | ||||||||||||||||||||||
100 | Efficient Projection-free Algorithms for Saddle Point Problems | Optimization -> Non-Convex Optimization | Optimization Methods (continuous or discrete) |